

@inproceedings{Bennett2019,
 author = {Bennett, A. and Kallus, N. and Schnabel, T.},
 booktitle = {Advances in Neural Information Processing Systems 32 ({NeurIPS})},
 publisher = {Curran Associates, Inc.},
 title = {Deep Generalized Method of Moments for Instrumental Variable Analysis},
 year = {2019}
}

@inproceedings{Muandet2020,
 author = {Muandet, K. and Mehrjou, A. and Lee, S. K. and Raj, A.},
 booktitle = {Advances in Neural Information Processing Systems 33 ({NeurIPS})},
 publisher = {Curran Associates, Inc.},
 title = {Dual Instrumental Variable Regression},
 year = {2020}
}

@inproceedings{Singh2019,
 author = {Singh, R. and Sahani, M. and Gretton, A.},
 booktitle = {Advances in Neural Information Processing Systems 32 ({NeurIPS})},
 publisher = {Curran Associates, Inc.},
 title = {Kernel Instrumental Variable Regression},
 year = {2019}
}
%editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
 
@InProceedings{Hartford2017,
  title = 	 {Deep {IV}: A Flexible Approach for Counterfactual Prediction},
  author =       {J. Hartford and G. Lewis and K. Leyton-Brown and M. Taddy},
  booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning ({ICML})},
  pages = 	 {1414--1423},
  year = 	 {2017},
  publisher =    {PMLR}
  }
  
  
@ARTICLE{Dunker2014,
title = {Iterative estimation of solutions to noisy nonlinear operator equations in nonparametric instrumental regression},
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journal = {Journal of Econometrics},
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pages = {444--455}
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@article{Imbens2009,
      title={Identification and Estimation of Triangular Simultaneous Equations Models without Additivity}, 
      author={Imbens, G. W. and  Newey, W. K.},
      year={2009},
      pages = {1481--1512},
      volume = {77},
      number = {5},
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@inproceedings{Saengkyongam2022,
  author = {S. Saengkyongam and  L. Henckel and N. Pfister and J. Peters},
  title = {Exploiting Independent Instruments: Identification and Distribution Generalization},
  booktitle = {Proceedings of the 39th International Conference on Machine Learning ({ICML}), accepted, ArXiv e-prints (2202.01864)},
  year = {2022}
}


@article{MOREIRA2009131,
title = {Tests with correct size when instruments can be arbitrarily weak},
journal = {Journal of Econometrics},
volume = {152},
number = {2},
pages = {131-140},
year = {2009},
note = {Nonparametric and Robust Methods in Econometrics},
issn = {0304-4076},
doi = {https://doi.org/10.1016/j.jeconom.2009.01.012},
url = {https://www.sciencedirect.com/science/article/pii/S0304407609000190},
author = {Marcelo J. Moreira},
}

@article{Kang2016,
author = {H. Kang and A. Zhang and T. Cai and D. S. Small},
title = {Instrumental Variables Estimation With Some Invalid Instruments and its Application to Mendelian Randomization},
journal = {Journal of the American Statistical Association},
volume = {111},
number = {513},
pages = {132-144},
year  = {2016},
publisher = {Taylor \& Francis}
}


@inproceedings{Mckeigue2010,
 author = {Mckeigue, P. and Krohn, J. and Storkey, A. J. and Agakov, F.},
 booktitle = {Advances in Neural Information Processing Systems ({NeurIPS})},
 publisher = {Curran Associates, Inc.},
 title = {Sparse Instrumental Variables (SPIV) for Genome-Wide Studies},
 volume = {23},
 year = {2010}
}
%editor = {J. Lafferty and C. Williams and J. Shawe-Taylor and R. Zemel and A. Culotta},
 

@ARTICLE{Belloni2012,
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@book{dhrymes2012econometrics,
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@book{amemiya1985advanced,
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@incollection{mariano2001simultaneous,
  title={Simultaneous equation model estimators: Statistical properties and practical implications},
  author={Mariano, R. S.},
  editor = {Badi H. Baltagi},
  booktitle={A companion to theoretical econometrics},
  pages={122--43},
  year={2001},
  publisher={Blackwell},
  address ={Malden, MA}
}

@article{moreira2009tests,
  title={Tests with correct size when instruments can be arbitrarily weak},
  author={Moreira, M. J.},
  journal={Journal of Econometrics},
  volume={152},
  number={2},
  pages={131--140},
  year={2009},
  publisher={Elsevier}
}

@article{anderson1950asymptotic,
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  pages={570--582},
  year={1950},
  publisher={Institute of Mathematical Statistics}
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%  number={4},

@article{anderson1949estimation,
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@article{davidson2014confidence,
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@Book{Hastie2009,
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@TECHREPORT{Heckerman1997,
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    title = {A {B}ayesian Approach to Causal Discovery},
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    year = {1997}
}
% entspricht Heckerman1999




@article{Haavelmo1944,
  author = {T. Haavelmo},
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@article{Aldrich1989,
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@article{Pan2010,
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@article{Dawid2010Didilez2010,
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journal = "Statistics Surveys",
pages = "184--231",
title = "Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview",
volume = "4",
year = "2010"
}
%publisher = "The American Statistical Association, the Bernoulli Society, the Institute of Mathematical Statistics, and the Statistical Society of Canada",
%url = "http://dx.doi.org/10.1214/10-SS081",
%journal = "Statist. Surv.",
%doi = "10.1214/10-SS081",


@article{Hoover1990,
    author = {K. D. Hoover},
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﻿@article{Nowzohour2015,
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   year = {2015}
}




@article{RasmussenNickisch10,
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year      = 2010,
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pages = {3011-3015}
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@article{Eberhardt2007,
    author = {F. Eberhardt and R. Scheines},
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    pages = {981--995},
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%    doi = {10.1086/525638},
%    url = {http://dx.doi.org/10.1086/525638},


@article{CauseEffectPairs,
author = {J. M. Mooij and D. Janzing},
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year = {2010}
}

@inproceedings{Mooij_et_al_NIPS_11,
  author    = {J. M. Mooij and D. Janzing and T. Heskes and B. Sch{\"o}lkopf},
  title     = {On Causal Discovery with Cyclic Additive Noise Models},
  booktitle = {{A}dvances in {N}eural {I}nformation {P}rocessing {S}ystems 24 ({NeurIPS})},
  year      = {2011}
}
%  editor    = {J. Shawe-Taylor and R.S. Zemel and P. Bartlett and F.C.N. Pereira and K.Q. Weinberger},
%pages     = {639--647},  
%  url       = {http://books.nips.cc/papers/files/nips24/NeurIPS2011_0449.pdf}




@article{Tamada2011,
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  journal = {Journal of Machine Learning Research},
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  title = {Parallel Algorithm for Learning Optimal {B}ayesian Network Structure.},
  volume = 12,
  year = 2011
}
%  url = {http://dblp.uni-trier.de/db/journals/jmlr/jmlr12.html#TamadaIM11},
  
  
@article{Tamada2011b,
  author = {Y. Tamada and S. Imoto and H. Araki and M. Nagasaki and C. G. Print and S. D. Charnock-Jones and S. Miyano},
  journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics}, 
  number = 3,
  pages = {683--697},
  title = {Estimating Genome-Wide Gene Networks Using Nonparametric {B}ayesian Network Models on Massively Parallel Computers.},
  volume = 8,
  year = 2011
}
%  url = {http://dblp.uni-trier.de/db/journals/tcbb/tcbb8.html#TamadaIANPCM11},


@book{Imbens2015,
 author = {Imbens, G. W. and Rubin, D. B.},
 title = {Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction},
 year = {2015},
 publisher = {Cambridge University Press},
 address = {New York, NY},
} 


@article{Rubin2005,
  title={Causal inference using potential outcomes},
  author={Rubin, D. B.},
  journal={Journal of the American Statistical Association},
      pages = {322--331},
  volume={100},
  year={2005}
}
%  number={469},

@article{SilvaGhahramani2009,
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year = 2009,
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@article{Fink2011,
	year={2011},
	journal={Journal of Algebraic Combinatorics},
	volume={33},
	title={The binomial ideal of the intersection axiom for conditional probabilities},
	publisher={Springer},
	author={A. Fink},
	pages={455--463}
}
%number={3},
%doi={10.1007/s10801-010-0253-5},
%url={http://dx.doi.org/10.1007/s10801-010-0253-5},

@article{Dawid79b,
     title = {Some Misleading Arguments Involving Conditional Independence},
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     pages = {249--252},
     year = {1979},
     publisher = {Wiley for the Royal Statistical Society}
    }
%     journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
%     url = {http://www.jstor.org/stable/2985039},
%     number = {2},



@article{Dawid80,
  author = {A. P. Dawid},
  fjournal = {Annals of Statistics},
  journal = {Annals of Statistics},
  pages = {598--617},
  title = {Conditional independence for statistical operations},
  volume = 8,
  year = 1980
}
%   number = 3,




@article{Dawid79,
     title = {Conditional Independence in Statistical Theory},
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     year = {1979},
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     url = {http://www.jstor.org/stable/2984718},
     ISSN = {00359246},




@TechReport{maaten,
  author =      {L.J.P. van der Maaten},
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  year =        {2007},
}

@Article{isomap,
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@inproceedings{Janzing2010b,
author = {D. Janzing and P.O. Hoyer and B. Sch\"{o}lkopf},
booktitle = {Proceedings of the 27th International Conference on Machine Learning ({ICML})},
title = {Telling cause from effect based on high-dimensional observations},
pages = {479--486},
year = {2010}
}



@book{Drton2009,
  publisher = {Birkh\"auser Verlag},
  series = {Oberwolfach Seminars},
Title = {Lectures on Algebraic Statistics},
Author = {M. Drton and B. Sturmfels and S. Sullivant},
Booktitle = {Oberwolfach Seminars},
Year = {2009},
Volume = {39}
}
%Exercise 6.6 in Chap 6


@book{Lauritzen1996,
  author = 	 {S. Lauritzen},
  title = 	 {Graphical Models},
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@InProceedings{Pickup14,
  author       = "Pickup, L. C. and Pan, Z. and Wei, D. and Shih, Y. and Zhang, C. and Zisserman, A. and Sch{\"o}lkopf, B. and Freeman, W. T.",
  title        = "Seeing the Arrow of Time",
  booktitle    = "IEEE Conference on Computer Vision and Pattern Recognition",
  year         = "2014",
}


@article{Doll50,
 author = {R. Doll and A. B. Hill},
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 pages = {739--748}
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@article{Druzdzel2001,
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 pages = {45--62}
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@misc{Mooij2008,
  author = {J. M. Mooij and D. Janzing and B. Sch{\"o}lkopf},
  title = {Distinguishing between cause and effect},
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﻿@article {Eichler2011,
   author = {M. Eichler},
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@ARTICLE{Nolte2008,
  AUTHOR =       {G. Nolte and A. Ziehe and V. Nikulin and A. Schl\"ogl and  N. Kr\"amer and T. Brismar and K.-R. M\"uller},
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  JOURNAL =      {Phys. Rev. Letters},
  YEAR =         {2008},
  volume =       {100}
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@book{Bowden1985, 
  series={Econometric Society Monographs}, 
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}
%collection={Econometric Society Monographs}
%place={Cambridge},  
%DOI={10.1017/CCOL0521262410}, 


@incollection{Wiener1956,
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@Article{Azzalini1990,
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@book{Box2008,
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@Article{Florens1982,
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@Article{Lucas,
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@book{Theil1958,
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@incollection{Mariano2001,
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  booktitle={A companion to theoretical econometrics},
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@article{Fuller1977,
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%  number={4},

@article{Chesher2003,
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@book{Woodward2003,
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@book{wooldridge2010econometric,
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@Article{Cartwright2003,
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@inproceedings{Hyvarinen2008,
 author = {A. Hyv\"{a}rinen and S. Shimizu and P.O. Hoyer},
 title = {Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-{G}aussianity},
  pages = {424--431},
  booktitle = {Proceedings of the 25th International Conference on Machine Learning ({ICML})},
 year = {2008}
} 
% pages = {424--431},
% address = {New York, NY, USA},



@Inproceedings{Povilas2010,
	title = {Inferring deterministic causal relations},
	author = {P. Daniusis and D. Janzing and J. M. Mooij and J. Zscheischler and B. Steudel and K. Zhang and B. Sch{\"o}lkopf},
        booktitle =  {Proceedings of the 26th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI})},
  publisher = {AUAI Press},
  address = {Corvallis, Oregon, USA},
	year = {2010}
}


@inproceedings{Mogensen2018,
  author    = {S. W. Mogensen and
               D. Malinsky and
               N. R. Hansen},
  title     = {Causal Learning for Partially Observed Stochastic Dynamical Systems},
  booktitle = {Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial
               Intelligence ({UAI})},
  pages     = {350--360},
  year      = {2018}
}
%  url       = {http://auai.org/uai2018/proceedings/papers/142.pdf},


@article{Mogensen2019,
    author = {S. W. Mogensen and N. R. Hansen},
    journal = {Annals of Statistics},
    title = {Markov equivalence of marginalized local independence graphs},
    volume = {(to appear)},
    year = {2019}
}
%    pages = {},
%number = {},
    

@article{Neyman1923,
  author = {Neyman, J.},
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  pages = {465--480},
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  year = 1923
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@article{Maathuis2009,
    author = {M.H. Maathuis and D. Colombo and M. Kalisch and P. B\"uhlmann},
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    number = {6A},
    pages = {3133--3164},
    title = {Estimating high-dimensional intervention effects from observational data},
    volume = {37},
    year = {2009}
}
%    keywords = {causality, regulatory\_networks},
%    posted-at = {2010-07-09 16:29:47},
%    priority = {3},
%    url = {http://dx.doi.org/this%20http%20URL},




@article{yu2019,
	author = {Yu, B. and Kumbier, K.},
	title = {Veridical data science},
	volume = {117},
	number = {8},
	pages = {3920--3929},
	year = {2020},
	publisher = {National Academy of Sciences},
	journal = {Proceedings of the National Academy of Sciences}
}
%	doi = {10.1073/pnas.1901326117},


@article{yu2013,
  title={Stability},
  author={Yu, B.},
  journal={Bernoulli},
  pages = {1484--1500}, 
  volume = {19},
  number = {4},
  year={2013}
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@article{Uhler2013,
  author = {C. Uhler and G. Raskutti and P. B\"uhlmann and B. Yu},
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  pages = {436--463},
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  volume = 41,
  number = 2,
  year = 2013
}


@inproceedings{Shpitser2010,
  author    = {I. Shpitser and T. J. Van der Weele and J. M. Robins},
  title     = {On the Validity of Covariate Adjustment for Estimating Causal Effects},
  booktitle = {Proceedings of the 26th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI})},
  year      = {2010},
  publisher = {AUAI Press},
  address = {Corvallis, Oregon, USA},
  pages     = {527--536}
}
%  ee        = {http://uai.sis.pitt.edu/displayArticleDetails.jsp?mmnu=1{\&}smnu=2{\&}article_id=2078{\&}proceeding_id=26},


@ARTICLE{Acid2003,
    author = {S. Acid and L. M. de Campos},
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  @Article{pcalg,
    title = {Causal Inference Using Graphical Models with the {R}
      Package {pcalg}},
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    pages = {1--26}
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@article{Textor2012,
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% doi = {10.1145/28395.28396},



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@incollection{deJongh2009,
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@incollection{Cartwright2009,
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}



@inproceedings{MooijJS2013,
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  pages = {440-448},
  year = {2013}
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@article{Tsamardinos2006,
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@book{Koller2009,
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@article{Janzing2012,
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  pages     = {1--31},
  year      = 2012,
}
%  issn      = {0004-3702},
%  doi       = {10.1016/j.artint.2012.01.002},
%  url       = {http://www.sciencedirect.com/science/article/pii/S0004370212000045}

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%ISSN = {00129682, 14680262},
% URL = {http://www.jstor.org/stable/2951620},
 

@book{Wright1928,
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%    month = may,
%   adsurl = {http://adsabs.harvard.edu/abs/2012arXiv1205.2536P},
%  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
%archivePrefix = "arXiv",
%   eprint = {1205.2536},
% primaryClass = "stat.ML",
% keywords = {Statistics - Machine Learning, Mathematics - Statistics Theory},


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%   timestamp = {2010-04-08T18:24:58.000+0200},
%  biburl = {http://www.bibsonomy.org/bibtex/227286880c0d0f7545d083309db3d72c0/for916},
%  doi = {10.1038/nmeth0410-247},
%  keywords = {C1},


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%issn = {1467-9639},
%url = {http://dx.doi.org/10.1111/1467-9639.00013},
%doi = {10.1111/1467-9639.00013},


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@incollection{Pils2015,
title = {Rate-Agnostic (Causal) Structure Learning},
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pages = {3303--3311},
year = {2015},
publisher = {Curran Associates, Inc.},
}
%editor = {C. Cortes and N. D. Lawrence and D. D. Lee and M. Sugiyama and R. Garnett},



@misc{Danks2013,
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@article{Buehlmann2012,
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@inproceedings{Mooij2013,
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  year      = 2013,
  pages     = {431--439}
}
%  location  = {Corvallis, Oregon},
%  publisher = {AUAI Press},
%  editor    = {Ann Nicholson and Padhraic Smyth},
%  url       = {http://auai.org/uai2013/prints/papers/23.pdf}

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@article{Stekhoven2012,
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  journal =       {submitted},
  year =          {2012}
}
%  OPTvolume =     {XY},
%  OPTnumber =     {Z},
%  OPTpages =      {xxx--yyy},



@INPROCEEDINGS{Ramsey2006,
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} 

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}
%  editor = {Christopher Meek and Uffe Kjærulff},
%  August 7-10 2003, Acapulco, Mexico

@inproceedings{Zscheischler2011,
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%  address = {Edinburgh, Scottland, UK},

@article{FriedmanKoller:MLJ03,
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}
%  number = {1--2},



@inbook{inflammations,
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@article{Janzing2010a,
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%  number =     {10},



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@article{Neurocomputing,
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@incollection{Magliacane2018,
title = {Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions},
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editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {10846--10856},
year = {2018},
publisher = {Curran Associates, Inc.}
}
%url = {http://papers.nips.cc/paper/8282-domain-adaptation-by-using-causal-inference-to-predict-invariant-conditional-distributions.pdf}

@InProceedings{Li2010,
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  year =      2010,
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}
%  note =      {(\href{http://arxiv.org/abs/1003.0146v2}{arXiv:1003.0146v2}.)}




@article{Lauritzen1990,
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@article{Zhang2008,
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  year      = {2008},
  pages     = {239--271}
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%  number    = {2},
%  ee        = {http://dx.doi.org/10.1007/s11023-008-9096-4},
%  bibsource = {DBLP, http://dblp.uni-trier.de}


@inproceedings{Zhang2007,
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@article{Rubenstein2016,
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@inproceedings{Meek1995,
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@inproceedings{Heckerman1999,
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@Book{Bollen1989,
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@book{Sutton2018,
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@Article{Sun2008,
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  pages =        {1248-1256},
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@book{MacKay2002,
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@book{Carroll2006,
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} 



  
@Article{Silva2006,
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% url = {http://projecteuclid.org/DPubS?service=UI\&\#38;version=1.0\&\#38;verb=Display\&\#38;handle=euclid.aos/1031689015},


@article{Hernan2006,
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@InCollection{Richardson1999,
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@INPROCEEDINGS{Geiger1994,
AUTHOR = {D. Geiger and D. Heckerman},
TITLE = {Learning {G}aussian networks},
booktitle = {Proceedings of the 10th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI})},
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address = {San Francisco, CA, USA},
PAGES = {235--243},
YEAR = {1994}
}


@InProceedings{RobinsLong,
author= {Robins, J. M.},
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%  publisher = {MIT Press},
%  url       = {http://books.nips.cc/papers/files/nips23/NeurIPS2010_1270.pdf}
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% ArXiv e-prints (1710.11469)





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%keywords = "Non-linear Granger causality",
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%url = {http://EconPapers.repec.org/RePEc:oup:biomet:v:90:y:2003:i:3:p:491-515}

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% editor = {J.C. Platt and D. Koller and Y. Singer and S. Roweis},
% publisher = {MIT Press},
% pages = {489--496},

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%  publisher =  {MIT Press}


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%  volume = 2,
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@Article{kankainen,
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  title = 	 {Consistent Testing of Total Independence Based on the Empirical Characteristic Function},
  journal = 	 {PhD Thesis, University of Jyv\"askyl\"a},
  year = 	 {1995},
}

@InProceedings{mmd_gretton,
  author =   {A. Gretton and K. Borgwardt and M. Rasch and B. Sch{\"o}lkopf and A. Smola},
  title =    {A Kernel Method for the Two-Sample-Problem},
  booktitle =    {Advances in Neural Information Processing Systems 19},
  year =     {2007},
  address =      {Cambridge, MA},
  pages = {513--520},
  publisher = {MIT Press}
}



@article{Gretton2012jmlr,
  title = {A Kernel Two-Sample Test },
  author = {Gretton, A. and Borgwardt, K. and Rasch, M. and Sch{\"o}lkopf, B. and Smola, A.},
  journal = {Journal of Machine Learning Research},
  volume = {13},
  pages = {723--773},
  year = {2012}
}


@book{nolan,
          author = {J.P. Nolan},
          title = {Stable Distributions - Models for Heavy Tailed Data},
          year = {2007},
          publisher = {Birkh\"auser},
          address = {Boston},
     }

@InProceedings{Gretton2005,
  author    = {A. Gretton and O. Bousquet and A. Smola and B. Sch\"{o}lkopf},
  title     = {Measuring Statistical Dependence with {H}ilbert-{S}chmidt Norms},
  booktitle =  {Algorithmic Learning Theory},
  publisher = {Springer-Verlag},
  year      = {2005},
  pages     = {63--78}
}

@InProceedings{hilbert_embedding,
  author    = {A.J. Smola and A. Gretton and L. Song and B. Sch\"{o}lkopf},
  title     = {A {H}ilbert Space Embedding for Distributions},
  booktitle =  {Algorithmic Learning Theory},
  year      = {2007},
  publisher = {Springer-Verlag},
  pages     = {13--31}
}

@InProceedings{barath,
 author     = {B. Sriperumbudur and A. Gretton and K. Fukumizu and G. Lanckriet and B. Sch{\"{o}}lkopf},
 title      = {Injective {H}ilbert Space Embeddings of Probability Measures},
 booktitle  = {Conference on Learning Theory},
 year       = {2008},
 note       = {to appear}
}
		  
@inProceedings{Krizhevsky2012,
title = {ImageNet Classification with Deep Convolutional Neural Networks},
author = {A. Krizhevsky and Sutskever, I. and G. E. Hinton},
booktitle = {Advances in Neural Information Processing Systems 25 ({NeurIPS})},
pages = {1097--1105},
year = {2012},
publisher = {Curran Associates, Inc.}
}
%editor = {F. Pereira and C. J. C. Burges and L. Bottou and K. Q. Weinberger},

@Article{mamai,
  author = 	 {L.V. Mamai},
  title = 	 {On the theory of characteristic functions},
  journal = 	 {Select. Transl. Math. Stat. Probab.},
  pages =        {153--170},
  volume =	 4,	  
  year = 	 1963
}


@book{alpha_stable,
          author = {G. Samorodnitsky and M.S. Taqqu},
          title = {Stable Non-{G}aussian Random Processes},
          year = {1994},
          publisher = {Chapman \& Hall},
          address = {New York},
     }


@article{hansen82,
  title={Large Sample Properties of Generalized Method of Moments Estimators},
  author={Hansen, L. P.},
  journal={Econometrica},
  volume={50},
  number={4},
  pages={1029--1054},
  year={1982}
}

@book{hall05,
  title={Generalized Method of Moments},
  author={Hall, A. R.},
  series={Advanced texts in econometrics},
  year={2005},
  publisher={Oxford University Press}
}

@incollection{newey94,
title = "Large sample estimation and hypothesis testing",
booktitle = "Handbook of Econometrics",
publisher = "Elsevier",
volume = "4",
pages = {2111 -- 2245},
year = "1994",
author = {W. K. Newey and D. McFadden}
}
% Chapter 36 



@article{Newey2013,
Author = {Newey, W. K.},
Title = {Nonparametric Instrumental Variables Estimation},
Journal = {American Economic Review},
Volume = {103},
Number = {3},
Year = {2013},
Pages = {550--556}
}


@Article{skitovich_trans,
  author = 	 {V. P. Skitovi{\v{c}}},
  title = 	 {Linear combinations of independent random variables and the normal distribution law},
  journal = 	 {Select. Transl. Math. Stat. Probab.},
  year = 	 {1962},
  volume = 	 {2},
  pages = 	 {211--228},
}

@Article{weiss,
  author = 	 {G. Weiss},
  title = 	 {Time-Reversibility of linear stochastic Processes},
  journal = 	 {J. Appl. Prob.},
  year = 	 {1975},
  volume = 	 {12},
  pages = 	 {831--836},
}


@misc{eeg_data3,
    title = {This data set (exp. IVa, subj. 3) can be downloaded after registration.},
    howpublished = {Website, 3.6.2008, 3:51pm},
    year = {2008},
    note = {\url{http://ida.first.fraunhofer.de/projects/bci/competition_iii/#datasets}}
}


@article{Butsic2015,
title = "Conservation and conflict in the Democratic Republic of Congo: The impacts of warfare, mining, and protected areas on deforestation",
journal = "Biological Conservation",
volume = "191",
pages = "266 -- 273",
year = "2015",
author = "V. Butsic and M. Baumann and A. Shortland and S. Walker and T. Kuemmerle"
}
%number = "",




@book{asymmetries,
          author = {P. Horwich},
          title = {Asymmetries in Time: Problems in the Philosophy of Science},
          year = {1987},
          publisher = {MIT},          
     }


@book{bertsekas,
          author = {D. Bertsekas},
          title = {Nonlinear Programming},
          year = {1999},
          publisher = {Athena Scientific},
          address = {Belmont},
	  edition = {second},
     }

@book{bertsimas,
          author = {D. Bertsimas and J.N. Tsitsiklis},
          title = {Introduction to Linear Optimization},
          year = {1997},
          publisher = {Athena Scientific},
          address = {Nashua},
    }

@book{borwein,
          author = {J. M. Borwein and A.S. Lewis},
          title = {Convex Analysis and Nonlinear Optimization},
          year = {2006},
          publisher = {Springer},
          address = {New York},
    }

@Article{cortvap,
  author = 	 {C. Cortez and V. Vapnik},
  title = 	 {Support vector networks},
  journal = 	 {Machine Learning},
  year = 	 {1995},
  volume = 	 {20},
  pages = 	 {273--297},
}

@Article{cramer,
  author = 	 {H. Cram\'er},
  title = 	 {\"Uber eine {E}igenschaft der normalen {V}erteilungsfunktion},
  journal = 	 {Math. Z.},
  year = 	 {1936},
  volume = 	 {41},
  pages = 	 {405--414},
}


@book{dasarathy,
  author = 	 {B.V. Dasarathy (editor)},
  title = 	 {Nearest Neighbor ({NN}) Norms: {NN} Pattern Classification Techniques},
  publisher = 	 {CA: IEEE Computer Society Press},
  year = 	 {1991},
  address = 	 {Los Alamitos},
}

@Article{davidsimon,
  author = 	 {S. Ben-David and H. Simon},
  title = 	 {Efficient learning of linear perceptrons},
  journal = 	 {Proceedings of Neural Information Processing Systems},
  year = 	 {2000},
  pages = 	 {189--195},
}

@book{dudley,
  author = 	 {R.M. Dudley},
  title = 	 {Real Analysis and Probability},
  publisher = 	 {Cambridge University Press},
  year = 	 {2002},
  address = 	 {New York, USA}
}


@Article{ellipsoid,
  author = 	 {M.K. Kozlov and S.P. Tarasov and L.G. Khachiyan},
  title = 	 {Polynomial solvability of convex quadratic programming},
  journal = 	 {Sov. Math.},
  year = 	 {1979},
  volume = 	 {20},
  pages = 	 {1108--1111},
}

@book{Agresti2002,
  author = {A. Agresti},
  publisher = {Wiley-Interscience},
  title = {Categorical Data Analysis},
  series = {Wiley Series in Probability and Statistics},
  edition = {2nd},
  year = {2002}
}

@article{Hyvaerinen1999,
 author = {A. Hyv\"{a}rinen and P. Pajunen},
 title = {Nonlinear independent component analysis: existence and uniqueness results},
 journal = {Neural Networks},
 volume = {12},
 number = {3},
 year = {1999},
 pages = {429--439},
 publisher = {Elsevier Science Ltd.},
 address = {Oxford, UK, UK},
 }
%  doi = {http://dx.doi.org/10.1016/S0893-6080(98)00140-3},

@Article{gretton_ind,
  author = 	 {A. Gretton and K. Fukumizu and C.H. Teo and L. Song and B. Sch\"olkopf and A.J. Smola},
  title = 	 {A Kernel Statistical Test of Independence},
  journal = 	 {Proceedings of Neural Information Processing Systems},
  year = 	 {2007},
  volume = 	 {20},
  pages = 	 {1--8},
}

@Article{gretton_TR,
  author = 	 {A. Gretton and K. Borgwardt and M.J. Rasch and B. Sch\"olkopf and A.J. Smola},
  title = 	 {A kernel Method for the Two-Sample-Problem},
  journal = 	 {Technical Report {\bf 157} MPI for biological Cybernetics, T\"ubingen},
  year = 	 {2008},
  volume = 	 {157},
  pages = 	 {1--8},
}


@article{Andersson1997,
  author = {S.A. Andersson and D. Madigan and M.D. Perlman},
  journal = {Annals of Statistics},
  pages = {505--541},
  title = {A characterization of {M}arkov equivalence classes for acyclic digraphs},
  volume = 25,
  year = 1997
}
%  number = 2,

@INPROCEEDINGS{Chickering1996,
    author = {D. M. Chickering},
    title = {Learning {B}ayesian Networks is {NP}-Complete},
    booktitle = {Learning from Data: Artificial Intelligence and Statistics V},
    year = {1996},
    publisher = {Springer-Verlag}
}
%    pages = {121--130},


@inproceedings{Chickering1995,
  author =	{D. M. Chickering},
  title =	{A Transformational Characterization of Equivalent {B}ayesian Network Structures},
  year =	{1995},
  publisher = {Morgan Kaufmann}, 
  address = {San Francisco, CA, USA},
  booktitle =   {Proceedings of the 11th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI})},
  pages =	{87--98}
}
 

@TECHREPORT{Heckerman1997,
    author = {D. Heckerman},
    title = {A {B}ayesian Approach to Causal Discovery},
    institution = {Microsoft Research (MSR-TR-97-05)},
    year = {1997}
}
% entspricht Heckerman1999

@TECHREPORT{Heckerman1996,
    author = {D. Heckerman},
    title = {A Tutorial on Learning With {B}ayesian Networks},
    institution = {Microsoft Research (MSR-TR-95-06)},
    year = {1997}
}
% enthaelt auch inferenz innerhalb eines bn



@article{Greenspan2016,
  author    = {H. Greenspan and
               B. van Ginneken and
               R. M. Summers},
  title     = {Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique},
  journal   = {{IEEE} Transactions of Medical Imaging},
  volume    = {35},
  number    = {5},
  pages     = {1153--1159},
  year      = {2016}
}



@article{Chickering2002,
 author = {D. M. Chickering},
 title = {Optimal structure identification with greedy search},
 journal = {Journal of Machine Learning Research},
 volume = {3},
 year = {2002},
 pages = {507--554}
} 
% url = {http://dx.doi.org/10.1162/153244303321897717},
% doi = {10.1162/153244303321897717},


@Article{hall_band,
  author = 	 {P. Hall and K.H. Kang},
  title = 	 {Bandwidth choice for nonparametric classification},
  journal = 	 {Annals of Statistics},
  year = 	 {2005},
  volume = 	 {3},
  number = 	 {1},
  pages = 	 {284--306},
}




@BOOK{Spirtes2000,
title = {Causation, Prediction, and Search},
edition = {2nd},
author = {P. Spirtes and C. Glymour and R. Scheines},
year = {2000},
publisher = {MIT Press}
}

@book{Koller09,
  title = {Probabilistic Graphical Models: Principles and Techniques},
  author = {D. Koller and N. Friedman},
  year = 2009,
  publisher = {MIT Press},
}


@book{learningkernels,
  author = 	 {B. Sch\"olkopf and A.J. Smola},
  title = 	 {Learning with Kernels},
  publisher = 	 {MIT Press},
  year = 	 {2002},
  address = 	 {Massachusetts},
}

@book{adv_kern_methods,
  author = 	 {B. Sch\"olkopf and C.J.C. Burges and A.J. Smola (editors)},
  title = 	 {Advances in Kernel Methods},
  publisher = 	 {MIT Press},
  year = 	 {1999},
  address = 	 {Massachusetts},
}



@Article{linnik,
  author = 	 {Y.V. Linnik},
  title = 	 {A problem concerning characteristic functions of probability distribution (in {R}ussian)},
  journal = 	 {Usp. Matem. Nauk},
  year = 	 {1955},
  volume = 	 {10},
  number = 	 {1},
  pages = 	 {137--138},
}

@Article{string,
  author = 	 {H. Lodhi and C. Saunders and J. Shawe-Taylor and N. Cristianini and C. Watkins},
  title = 	 {Text classification using string kernels},
  journal = 	 {Journal of Machine Learning Research},
  year = 	 {2002},
  volume = 	 {2},
  pages = 	 {419--444},
}

@book{loeve,
  author = 	 {M. Loeve},
  title = 	 {Probability Theory},
  publisher = 	 {Van Nostrand},
  edition = 	 {second},
  year = 	 {1960},
  address = 	 {Princeton},
}


﻿@article{Nowzohour2013,
   author = {C. Nowzohour and P. B\"uhlmann},
   title = {Score-based Causal Learning in Additive Noise Models},
   journal = {ArXiv e-prints (1311.6359)},
   year = {2013}
}



@article{Evans2018,
author = {R. J. Evans},
year = {2015},
title = {Margins of discrete {B}ayesian networks},
volume = {46}, 
number = {6A}, 
pages = {2623--2656},
journal = {Annals of Statistics}
}


@article{ShpitserIntroToNested2014,
  author = {I. Shpitser and R. J. Evans and T. S. Richardson and J. M. Robins },
  title = {Introduction to Nested {M}arkov Models},
  journal = {Behaviormetrika},
  volume = {41},
  pages = {3--39},
  year = {2014}
}


@article{Richardson2017,
   author = {T. S. Richardson and R. J. Evans and J. M. Robins and I. Shpitser},
   title = {Nested Markov Properties for Acyclic Directed Mixed Graphs},
   journal = {ArXiv e-prints (1701.06686)},
   year = {2017}
}

@article{Wang2017,
   author = {Y. Wang, L. Solus, K. D. Yang, C. Uhler},
    title = {Permutation-based Causal Inference Algorithms with Interventions},
  journal = {ArXiv e-prints (1705.10220)},
     year = 2017,
}


@Article{stoch_pr,
  author = 	 {M.N. Lukic and J.H. Beder},
  title = 	 {Stochastic Processes With Sample Paths In Reproducing Kernel {H}ilbert Spaces},
  journal = 	 {Trans. Am. Math. Soc.},
  year = 	 {2001},
  volume = 	 {353},
  pages = 	 {3945--3969},
}


@Article{ica_paper,
  author = 	 {P. Comon},
  title = 	 {Independent component analysis -- a new concept?},
  journal = 	 {Signal Processing},
  volume = 	 {36},
  pages = 	 {287--314},
  year = 	 {1994},
}


@Article{nuclass,
  author = 	 {B. Sch\"olkopf and A.J. Smola and R.C. Williamson and P.L. Bartlett},
  title = 	 {New support vector algorithms},
  journal = 	 {Neural Computation},
  year = 	 {2000},
  volume = 	 {12},
  pages = 	 {1207--1245},
}

@book{ramachandran,
  author = 	 {B. Ramachandran},
  title = 	 {Advanced Theory of Characteristic Functions},
  publisher = 	 {Statistical Publishing Society},
  year = 	 {1967},
  address = 	 {Calcutta},
}



@book{rao,
  author = 	 {C.R. Rao},
  title = 	 {Linear Statistical Inference and Its Applications},
  publisher = 	 {John Wiley \& Sons Inc},
  year = 	 {2002},
  address = 	 {USA},
}

@book{ripley,
  author = 	 {B.D. Ripley},
  title = 	 {Pattern Recognition and Neural Networks},
  publisher = 	 {Cambridge University Press},
  year = 	 {1996},
  address = 	 {Cambridge},
}

@book{serfling,
  author = 	 {R.J. Serfling},
  title = 	 {Approximation Theorems of Mathematical Statistics},
  publisher = 	 {John Wiley \& Sons Inc},
  year = 	 {2002},
  address = 	 {USA},
}


@book{Tong1990,
  title={Non-Linear Time Series: A Dynamical System Approach},
  author={H. Tong},
  series={Oxford Statistical Science Series},
  year={1990},
  publisher={Clarendon Press}
}
%  isbn={9780198523000},
%  lccn={89029697},
%  url={http://books.google.ch/books?id=d1RHjvDAye0C},


@Article{skitovich,
  author = 	 {V.P. Skitovi{\v{c}}},
  title = 	 {Linear Forms in independent random variables and the normal distribution law (in {R}ussian)},
  journal = 	 {Izvestiia AN SSSR, Ser. Matem.},
  year = 	 {1954},
  volume = 	 {18},
  pages = 	 {185--200},
}


@ARTICLE{Daudin1980,
title = {Partial association measures and an application to qualitative regression},
author = {J. J. Daudin},
year = {1980},
journal = {Biometrika},
volume = {67},
number = {3},
pages = {581--590},
}


@book{IPCC4WG2,
    booktitle = {Fourth Assessment Report of the Intergovernmental Panel on Climate Change},
    editor = {M.L. Parry and O.F. Canziani and J.P. Palutikof and P.J. van der Linden and C.E. Hanson},
    publisher = {Cambridge University Press},
    title = {{IPCC Fourth Assessment Report: Working Group II Report ``Impacts, Adaptation and Vulnerability''}},
    url = {http://www.ipcc.ch/ipccreports/ar4-wg2.htm},
    year = {2007}
}
%author = {IPCC},
    

@misc{ASOSdata,
  author = {NCDC},
  title = {{ASOS} 1-Minute Data ({DSI} 6406/page 2 data)},
  year = {2009},
  url = {http://www.ncdc.noaa.gov/oa/climate/climatedata.html},
  institution = {National Climatic Data Center ({US} Dept. of Commerce)} 
} 


@article{Shimizu2011,
 author = {S. Shimizu and T. Inazumi and Y. Sogawa and A. Hyv\"{a}rinen and Y. Kawahara and T. Washio and P.O. Hoyer and K. Bollen},
 title = {Direct{LiNGAM}: A Direct Method for Learning a Linear Non-{G}aussian Structural Equation Model},
 journal = {Journal of Machine Learning Research},
 volume = {12},
 year = {2011},
 pages = {1225--1248},
 publisher = {JMLR.org},
} 
% url = {http://dl.acm.org/citation.cfm?id=1953048.2021040},


@article{Hyttinen2012,
 author = {A. Hyttinen and F. Eberhardt and P. O. Hoyer},
 title = {Learning Linear Cyclic Causal Models with Latent Variables},
 journal = {Journal of Machine Learning Research},
 volume = {13},
 year = {2012},
 number = {1},
 pages = {3387--3439}
} 


@misc{RaduMaster,
    author = {R.P. Tanase}, 
    title = {Learning Causal Structures from {G}aussian Structural Equation Models},
    howpublished = {Master Thesis (ETH Zurich)},
    note = {available online},
    year = {2012}
}


@ARTICLE{Hauser2012,
  author = {A. Hauser and P. B{\"u}hlmann},
  title = {Characterization and greedy learning of interventional {M}arkov equivalence classes of directed acyclic graphs},
  journal = {Journal of Machine Learning Research},
  year = {2012},
  volume = {13},
  pages = {2409--2464}
}


@misc{spam,
    title = {Netzzeitung},
    howpublished = {Website, 17.4.2008, 2:55pm},
    year = {2008},
    note = {\url{http://www.netzeitung.de/internet/976597.html}}
}


@article{Haughton1988,
    author = {D.M.A. Haughton},
    journal = {Annals of Statistics},
    pages = {342--355},
    title = {On the Choice of a Model to Fit Data from an Exponential Family},
    volume = {16},
    year = {1988}
}
%    number = {1},
%    url = {http://www.jstor.org/stable/2241441},



@Article{steinwart,
  author = 	 {I. Steinwart},
  title = 	 {On the influence of the kernel on the consistency of support vector machines},
  journal = 	 {Journal of Machine Learning Research},
  year = 	 {2002},
  volume = 	 {2},
  pages = 	 {67--93},
}


@PhDThesis{Meek1997,
  Author="C. Meek",
  School="Carnegie Mellon University",
  Title="Graphical Models: Selecting Causal and Statistical Models",
  Year="1997",
  Address="Pittsburgh, PA"
}



@book{wendland,
  author = 	 {H. Wendland},
  title = 	 {Scattered Data Approximation},
  publisher = 	 {Cambridge University Press},
  year = 	 {2005},
  address = 	 {Cambridge},
}

@book{werner,
  author = 	 {D. Werner},
  title = 	 {Funktionalanalysis},
  publisher = 	 {Springer},
  year = 	 {2007},
  edition = 	 {sixth},
  address = 	 {Berlin},
}


@Article{zinger,
  author = 	 {A.A. Zinger and Y.V. Linnik},
  title = 	 {An analytic extension of {C}ram\'er's theorem and its application (in {R}ussian)},
  journal = 	 {Vestnik Leningrad. Univ.},
  year = 	 {1955},
  volume = 	 {11},
  pages = 	 {51--56},
}

@Article{jbtest,
  author = 	 {C.M. Jarque and A.K. Bera},
  title = 	 {A test for normality of observations and regression residuals},
  journal = 	 {International Statistical Review},
  year = 	 {1987},
  volume = 	 {55},
  number = 	 {2},
  pages = 	 {163--172},
}

@book{Gyorfi++2002,
  author = {L{\'a}szl{\'o} Gy{\"o}rfi and Michael Kohler and Adam Krzy{\.z}ak and Harro Walk},
  title = {A Distribution-Free Theory of Nonparametric Regression},
  year = 2002,
  publisher = {Springer},
  address = {New York}
}







































%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Jonas' Publications
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

@article{Runge2019,
  author = {J. Runge and S. Bathiany and E. Bollt and G. Camps-Valls and D. Coumou and E. Deyle and C. Glymour and M. Kretschmer and M. Mahecha and J. Munoz-Mari and E. Van Nes and J. Peters and R. Quax and M. Reichstein and M. Scheffer and B. Schoelkopf and P. Spirtes and G. Sugihara and J. Sun and K. Zhang and J. Zscheischler},
  title = {Inferring causation from time series in {E}arth system sciences},
  journal = {Nature Communications},
  volume = {10}, 
  number = {2553}, 
  year = {2019}
}



@article{Christiansen2018,
  author = {R. Christiansen and J. Peters},
  title = {Invariant Causal Prediction in the Presence of Latent Variables},
  journal = {Journal of Machine Learning Research},
  volume = {21},
  number = {41},
  pages = {1--46}, 
  year = {2020}
}


@article{Shah2018,
  author = {R. Shah and J. Peters},
  title = {The Hardness of Conditional Independence Testing and the Generalised Covariance Measure},
  journal = {Annals of Statistics},
  volume = {48},
  number = {3},
  pages = {1514--1538},
  year = {2020}
}
%ArXiv e-prints (1804.07203)

@article{Rothenhaeusler2018,
  author = {D. Rothenh\"ausler and P. B\"uhlmann and N. Meinshausen and J. Peters},
  title = {Anchor regression: heterogeneous data meets causality},
  journal = {Journal of Royal Statistical Society, Series B},
  volume = {83},
  number = {2},
  pages = {215--246},
  year = {2021}
}
%; ArXiv e-prints (1801.06229)

@article{HeinzeDeml2017,
  author = {C. Heinze-Deml and J. Peters and N. Meinshausen},
  title = {Invariant Causal Prediction for Nonlinear Models},
  journal = {Journal of Causal Inference},
  volume = {6},
  number = {2}, 
  pages = {1--35},
  year = {2018}
}

@article{Bongers2016,
  author = {S. Bongers and J. Peters and B. Sch\"olkopf and J. M. Mooij},
  title = {Structural Causal Models: Cycles, Marginalizations, Exogenous Reparametrizations and Reductions},
  journal = {ArXiv e-prints (1611.06221)},
  year = {2016}
}


@article{Bongers2016b,
  author = {S. Bongers and J. Peters and B. Sch\"olkopf and J. M. Mooij},
  title = {Theoretical Aspects of Cyclic Structural Causal Models},
  journal = {ArXiv e-prints (1611.06221v2)},
  year = {2016}
}




@article{Lundborg2020,
  author = {A. R. Lundborg and R. D. Shah and J. Peters},
  title = {Conditional Independence Testing in {H}ilbert Spaces with Applications to Functional Data Analysis},
  journal = {ArXiv e-prints (2101.07108)},
  year = {2020}
}


@article{Bongers2021,
  author = {S. Bongers and P. Forre and J. Peters and J. M. Mooij},
  title = {Foundations of Structural Causal Models with Cycles and Latent Variables},
  journal = {Annals of Statistics},
  volume = {49}, 
  number = {5}, 
  pages = {2885--2915},
  year = {2021}
}
%ArXiv e-prints (1611.06221v4)
@techreport{Ver93,
  author = {T. S. Verma},
  title = {Graphical Aspects of Causal Models},
  number = {R-191},
  institution = {Computer Science Department, University of California},
  address = {Los Angeles, CA},
  year = 1993
}
%  url = {http://ftp.cs.ucla.edu/pub/stat_ser/r191.pdf}


@article{richardson2003markov,
  title={Markov properties for acyclic directed mixed graphs},
  author={Richardson, T.},
  journal={Scandinavian Journal of Statistics},
  volume={30},
  number={1},
  pages={145--157},
  year={2003},
  publisher={Wiley Online Library}
}

@article{Gnecco2021,
  author = {N. Gnecco and N. Meinshausen and J. Peters and S. Engelke},
  title = {Causal discovery in heavy-tailed models},
  journal = {Annals of Statistics},
  volume = {49},
  number = {3},
  pages = {1755--1778},
  year = {2021}
}

@article{Pfister2018arxiv,
  author = {N. Pfister and S. Bauer and J. Peters},
  title = {Identifying Causal Structure in Large-Scale Kinetic Systems},
  journal = {ArXiv e-prints (1810.11776)},
  year = {2018}
}


@article{Peters2020dynamics,
  author = {J. Peters and S. Bauer and N. Pfister},
  title = {Causal models for dynamical systems},
  journal = {ArXiv e-prints (2001.06208)},
  year = {2020}
}


@article{Pfister2019stab,
  author = {N. Pfister and E. G. William and J. Peters and R. Aebersold and P. B{\"u}hlmann},
  title = {Stabilizing Variable Selection and Regression},
  journal = {Annals of Applied Statistics},
  volume = {15},
  number = {3},
  pages = {1220--1246},
  year = {2021}
}
%ArXiv e-prints (1911.01850)},

@article{Christiansen2020DG,
  author = {R. Christiansen and N. Pfister and M. Jakobsen and N. Gnecco and J. Peters},
  title = {The Difficult Task of Distribution Generalization in Nonlinear Models},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (accepted); ArXiv e-prints (2006.07433)},
  year = {2020}
}

% arxiv: http://arxiv.org/abs/2103.02477
@inproceedings{Oberst2021,
  author = {M. Oberst and N. Thams and J. Peters and D. Sontag},
  title = {Regularizing towards Causal Invariance: Linear Models with Proxies},
  booktitle = {Proceedings of the 38th International Conference on Machine Learning ({ICML})},
  pages = {8260--8270},
  year = {2021}
}



@article{Christiansen2021,
  author = {R. Christiansen and  M. Baumann and T. Kümmerle and M. Mahecha and J. Peters},
  title = {Towards Causal Inference for Spatio-Temporal Data: Conflict and Forest Loss in {C}olombia},
  journal = {Journal of the American Statistical Association (accepted), ArXiv e-prints (2005.08639)},
  year = {2021}
}


%article{Jakobsen2020old,
%  author = {M. Jakobsen and J. Peters},
%  title = {Distributional Robustness of K-class Estimators and the {PULSE}},
%  journal = {The Econometrics Journal (accepted), ArXiv e-prints (2005.03353)},
%  year = {2021}
%}

@article{Jakobsen2020,
  author = {M. Jakobsen and J. Peters},
  title = {Distributional Robustness of K-class Estimators and the {PULSE}},
  journal = {The Econometrics Journal},
  year = {2022},
  volume = {25}, 
  number = {2},
  pages ={404--432}  
}
%ArXiv e-prints (2005.03353)


@article{Weichwald2020,
  author = {S. Weichwald and J. Peters},
  title = {Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness},
  journal = {Journal of Cognitive Neuroscience},
  volume = {33},
  number = {2},
  pages = {226--247},
  year = {2021}
}
%   ArXiv e-prints (2002.06060)},



@article{Sachs,
  author = {K. Sachs and O. Perez and D. Pe'er D. A. Lauffenburger and G. P. Nolan},
  title = {Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data},
  volume = {308},
  number = {5721},
  pages = {523--529},
  year = {2005},
  journal = {Science}
}


@article{Pfister2019pnas,
  author = {N. Pfister and S. Bauer and J. Peters},
  title = {Learning stable and predictive structures in kinetic systems},
  volume = {116},
  number = {51},
  pages = {25405--25411},
  year = {2019},
  publisher = {National Academy of Sciences},
  journal = {Proceedings of the National Academy of Sciences}
}


@inproceedings{shpitser2008dormant,
  title={Dormant independence},
  booktitle = {Proceedings of the 23rd AAAI Conference on Artificial Intelligence},
  author={Shpitser, Ilya and Pearl, Judea},
  year={2008},
  pages = {1081--1087},
  publisher={AAAI Press}
}


@article{Yu3920,
	author = {Yu, B. and Kumbier, K.},
	title = {Veridical data science},
	volume = {117},
	number = {8},
	pages = {3920--3929},
	year = {2020},
	publisher = {National Academy of Sciences},
	journal = {Proceedings of the National Academy of Sciences}
}

@article{Mahecha2020,
AUTHOR = {Mahecha, M. D. and Gans, F. and Brandt, G. and Christiansen, R. and Cornell, S. E. and Fomferra, N. and Kraemer, G. and Peters, J. and Bodesheim, P. and Camps-Valls, G. and Donges, J. F. and Dorigo, W. and Estupinan-Suarez, L. M. and Gutierrez-Velez, V. H. and Gutwin, M. and Jung, M. and Londo\~no, M. C. and Miralles, D. G. and Papastefanou, P. and Reichstein, M.},
TITLE = {Earth system data cubes unravel global multivariate dynamics},
JOURNAL = {Earth System Dynamics},
VOLUME = {11},
YEAR = {2020},
NUMBER = {1},
PAGES = {201--234}
}
%URL = {https://www.earth-syst-dynam.net/11/201/2020/},
%DOI = {10.5194/esd-11-201-2020}

@book{Peters2021book,
  author = 	 {J. Peters and N. Meinshausen},
  title = 	 {The Raven's Hat: Fallen Pictures, Rising Sequences, and Other Mathematical Games},
  publisher =    {MIT Press},
  year = 	 {2021},
  address = 	 {Cambridge, MA, USA}
}


@book{Peters2017book,
  author = 	 {J. Peters and D. Janzing and B. Sch\"olkopf},
  title = 	 {Elements of Causal Inference: Foundations and Learning Algorithms},
  publisher =    {MIT Press},
  year = 	 {2017},
  address = 	 {Cambridge, MA, USA}
}
%  note = {can be downloaded at \url{http://www.math.ku.dk/~peters/}},


@INPROCEEDINGS{Meinshausen2018,
  author={N. Meinshausen},
  booktitle={IEEE Data Science Workshop}, 
  title={Causality from a distributional robustness point of view}, 
  year={2018},
  pages={6--10}
}
%  volume={},
%  number={},



@article{Rojas2016,
  author = {M. Rojas-Carulla and B. Sch\"olkopf and R. Turner and J. Peters},
  title = {Causal Transfer in Machine Learning},
  journal = {Journal of Machine Learning Research},
  volume = {19},
  number = {36},
  pages = {1--34},
  year = {2018}
}

@article{Pfister2018jasa,
  author = {N. Pfister and P. B\"uhlmann and J. Peters},
  title = {Invariant Causal Prediction for Sequential Data},
  journal = {Journal of the American Statistical Association},
  volume = {114},
  number = {527},
  pages = {1264--1276},
  year = {2018}
}
%ArXiv e-prints (1706.08058)


@article {Pfister2017jrssb,
author = {Pfister, N. and B\"uhlmann, P. and Sch\"olkopf, B. and Peters, J.},
title = {Kernel-based tests for joint independence},
journal = {Journal of the Royal Statistical Society: Series B},
volume = {80},
pages = {5--31},
year = {2017}
}
%doi = {10.1111/rssb.12235},




@article{Meinshausen2016pnas,
author = {N. Meinshausen and A. Hauser and J. Mooij and J. Peters and P. Versteeg and P. B\"uhlmann},
title = {Methods for Causal inference from gene perturbation experiments and validation},
journal = {Proceedings of the National Academy of Sciences}, 
volume = {113},
number = {27},
pages = {7361--7368},
year = {2016}
}



@article{Bareinboim2016pnas,
author = {E. Bareinboim, J. Pearl},
title = {Causal inference and the data-fusion problem},
journal = {Proceedings of the National Academy of Sciences}, 
volume = {113},
number = {27},
pages = {7345--7352},
year = {2016}
}



@article{Schoelkopf2016pnas,
author = {B. Sch\"olkopf and D. Hogg and D. Wang and D. Foreman-Mackey and D. Janzing and C.-J. Simon-Gabriel and J. Peters},
title = {Modeling Confounding by Half-Sibling Regression},
journal = {Proceedings of the National Academy of Sciences}, 
volume = {113},
number = {27},
pages = {7391--7398},
year = {2016}
}

@article{Peters2016jrssb,
author = {J. Peters and P. B\"uhlmann and N. Meinshausen},
year = {2016},
title = {Causal inference using invariant prediction: identification and confidence intervals},
volume = {78},
number = {5},
pages = {947--1012},
journal = {Journal of the Royal Statistical Society: Series B (with discussion)}
}
% ArXiv e-prints (1501.01332)

@inproceedings{Bauer2016icml,
author = {S. Bauer and B. Sch\"olkopf and J. Peters},
title = {The Arrow of Time in Multivariate Time Series},
booktitle = {Proceedings of the 33rd International Conference on Machine Learning ({ICML})},
pages = {2043--2051},
publisher = {Journal of Machine Learning Research: Workshop and Conference Proceedings},
year = {2016},
}

@article{Mooij2016jmlr,
author = {J. M. Mooij and J. Peters and D. Janzing and J. Zscheischler and B. Sch\"olkopf},
year = {2016},
volume = {17},
number = {32},
pages = {1--102},
title = {Distinguishing cause from effect using observational data: methods and benchmarks},
journal = {Journal of Machine Learning Research}
}

@article{Sippel2015grl,
title = {Quantifying changes in climate variability and extremes: Pitfalls and their overcoming},
author = {S. Sippel and J. Zscheischler and M. Heimann and F. E. L. Otto and J. Peters and M. D. Mahecha},
journal = {Geophysical Research Letters},
volume = {42},
number = {22},
pages = {9990--9998},
year = {2015}
}

@inproceedings{Rothenhaeusler2015,
title = {{backShift}: Learning causal cyclic graphs from unknown shift interventions},
author = {D. Rothenh{\"a}usler and C. Heinze and J. Peters and N. Meinshausen},
booktitle = {Advances in Neural Information Processing Systems 28 ({NeurIPS})},
pages = {1513--1521},
publisher = {Curran Associates, Inc.},
year = {2015}
}

@inproceedings{Schoelkopf2015icml,
author = {B. Sch{\"o}lkopf and D. W. Hogg and D. Wang and D. Foreman-Mackey and D. Janzing and C.-J. Simon-Gabriel and J. Peters},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning ({ICML})},
title = {Removing systematic errors for exoplanet search via latent causes},
pages = {2218--2226},
publisher = {ACM Press},
year = {2015}
}
% address = {New York, NY, USA},

@article{Schoelkopf2015standcomp,
title = {Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations},
author = {B. Sch{\"o}lkopf and K. Muandet and K. Fukumizu and S. Harmeling and J. Peters},
journal = {Statistics and Computing},
volume = {25},
number = {4},
pages = {755--766},
year = {2015}
}

@article{Peters2015neco,
author = {J. Peters and P. B{\"u}hlmann},
title = {Structural Intervention Distance ({SID}) for Evaluating Causal Graphs},
journal = {Neural Computation},
volume = {27},
pages = {771--799},
year = {2015}
}
% ArXiv e-prints (1306.1043)

@article{migliavacca2021eco,
author = {M. Migliavacca and T. Musavi and M. D. Mahecha and J. A. Nelson and J. Knauer and D. D. Baldocchi and O. Perez-Priego and R. Christiansen and J. Peters and K. Anderson and M. Bahn and T. A. Black and P. D. Blanken and D. Bonal and N. Buchmann and S. Caldararu and A. Carrara and N. Carvalhais and A. Cescatti and J. Chen and J. Cleverly and E. Cremonese and A. R. Desai and T. S. El-Madany and M. M. Farella and M. Fernández-Martínez and G. Filippa and M. Forkel and M. Galvagno and U. Gomarasca and C. M. Gough and M. Göckede and A. Ibrom and H. Ikawa and I. A. Janssens and M. Jung and J. Kattge and T. F. Keenan and A. Knohl and H. Kobayashi and G. Kraemer and B. E. Law and M. J. Liddell and X. Ma and I. Mammarella and D. Martini and C. Macfarlane and G. Matteucci and L. Montagnani and D. E. Pabon-Moreno and C. Panigada and D. Papale and E. Pendall and J. Penuelas and R. P. Phillips and P. B. Reich and M. Rossini and E. Rotenberg and R. L. Scott and C. Stahl and U. Weber and G. Wohlfahrt and S. Wolf and I. J. Wright and D. Yakir and S. Zaehle and M. Reichstein},
year = {2021},
title = {The three major axes of terrestrial ecosystem function},
journal = {Nature (accepted)},
pages = {x--x},
volume = {x}
}


@article{Peters2014jci,
author = {J. Peters},
year = {2014},
title = {On the Intersection Property of Conditional Independence and its Application to Causal Discovery},
journal = {Journal of Causal Inference},
pages = {97--108},
volume = {3}
}


@article{Buehlmann2014annals,
author = {P. {B{\"u}hlmann} and J. Peters and J. Ernest},
title = {{CAM}: Causal Additive Models, high-dimensional order search and penalized regression},
journal = {Annals of Statistics}, 
volume = {42},
pages = {2526--2556},
year = {2014}
}



@article{MeinshausenMiniMax,
author = {Meinshausen, N. and B{\"u}hlmann, P.},
title = {Maximin effects in inhomogeneous large-scale data},
journal = {Annals of Statistics}, 
volume = {43},
number = {4},
pages = {1801--1830},
year = {2015}
}


@article{Peters2014jmlr,
author = {J. Peters and J. M. Mooij and D. Janzing and B. Sch\"olkopf},
title = {Causal Discovery with Continuous Additive Noise Models}, 
year = {2014},
journal = {Journal of Machine Learning Research},
volume = {15},
pages = {2009--2053}
}




@inproceedings{Peters2013nips,
author = {J. Peters and D. Janzing and B. Sch\"olkopf},
title = {Causal Inference on Time Series using Structural Equation Models},
booktitle = {{A}dvances in {N}eural {I}nformation {P}rocessing {S}ystems 26 ({NeurIPS})}, 
pages = {585--592},
publisher = {Curran Associates, Inc.},
year = {2013}
}

@article{Peters2014biometrika,
author = {J. Peters and P. B{\"u}hlmann},
title = {Identifiability of {G}aussian Structural Equation Models with Equal Error Variances},
journal = {Biometrika},
pages = {219--228},
volume = {101},
number = {1},
year = {2014}
}

@article{Bottou2013jmlr,
author = {L. Bottou and J. Peters and J. Qui{\~n}onero-Candela and D. X. Charles and D. M. Chickering and E. Portugualy and D. Ray and P. Simard and E. Snelson},
title = {Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising},
journal = {Journal of Machine Learning Research},
volume = {14},
pages = {3207--3260},
year = {2013}
}

@inproceedings{Sgouritsa2013uai,
author = {E. Sgouritsa and D. Janzing and J. Peters and B. Sch\"{o}lkopf},
title = {Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders},
booktitle = {Proceedings of the 29th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI})},
pages = {556--565},
publisher = {AUAI Press},
year = {2013}
}
% address = {Corvallis, Oregon, USA},


@inproceedings{Zhang2018uai,
author = {K. Zhang and M. Gong and J. Ramsey and K. Batmanghelich and P. Spirtes and C. Glymour},
title = {Causal Discovery with Linear Non-Gaussian Models underMeasurement Error:  Structural Identifiability Results},
booktitle = {Proceedings of the 34th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI})},
publisher = {AUAI Press},
year = {2018}
}
% address = {Corvallis, Oregon, USA},





@inproceedings{Schoelkopf2012icml,
author = {B. Sch\"{o}lkopf and D. Janzing and J. Peters and E. Sgouritsa and K. Zhang and J. M. Mooij},
booktitle = {Proceedings of the 29th International Conference on Machine Learning ({ICML})},
title = {On causal and anticausal learning},
pages = {1255--1262},
publisher = {Omnipress},
year = {2012}
}
% editor = {John Langford and Joelle Pineau},
% address = {New York, NY, USA},

@inproceedings{Peters2011uai,
author = {J. Peters and J. M. Mooij and D. Janzing and B. Sch\"{o}lkopf},
title = {Identifiability of Causal Graphs using Functional Models},
booktitle = {Proceedings of the 27th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI})},
pages = {589--598},
publisher = {AUAI Press},
year = {2011}
}
% address = {Corvallis, Oregon, USA},


@inproceedings{Claassen2013,
  title     = {Learning Sparse Causal Models is not {NP}-hard},
  author    = {T. Claassen and J. M. Mooij and T. Heskes},
  booktitle = {Proceedings of the 29th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI})},
  year      = {2013},
  publisher = {AUAI Press},
  pages     = {172--181}
}
%  location  = {Corvallis, Oregon},
%  url       = {http://auai.org/uai2013/prints/papers/121.pdf}
%  editor    = {Ann Nicholson and Padhraic Smyth},

@inproceedings{Janzing2011uai,
author = {D. Janzing and E. Sgouritsa and O. Stegle and J. Peters and B. Sch\"{o}lkopf},
title = {Detecting low-complexity unobserved causes},
booktitle = {Proceedings of the 27th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI})},
pages = {383--391},
publisher = {AUAI Press},
year = {2011}
}
% address = {Corvallis, Oregon, USA},

@article{berrett2020conditional,
  title={The conditional permutation test for independence while controlling for confounders},
  author={Berrett, Thomas B and Wang, Yi and Barber, Rina Foygel and Samworth, Richard J},
  journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  volume={82},
  number={1},
  pages={175--197},
  year={2020},
  publisher={Wiley Online Library}
}

@inproceedings{Zhang2011uai,
author = {K. Zhang and J. Peters and D. Janzing and B. Sch{\"o}lkopf},
title = {Kernel-based Conditional Independence Test and Application in Causal Discovery},
booktitle = {Proceedings of the 27th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI})},
pages = {804--813},
publisher = {AUAI Press},
year = {2011}
}
%address = {Corvallis, Oregon, USA},


@inproceedings{Zhang2011uaishort,
author = {K. Zhang and J. Peters and D. Janzing and B. Sch{\"o}lkopf},
title = {Kernel-based Conditional Independence Test and Application in Causal Discovery},
booktitle = {{UAI} 27},
pages = {804--813},
year = {2011}
}


@article{Peters2011tpami,
author = {J. Peters and D. Janzing and B. Sch\"{o}lkopf},
title = {Causal Inference on Discrete Data Using Additive Noise Models},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {33},
year = {2011},
pages = {2436--2450},
publisher = {IEEE Computer Society}
}
%address = {Los Alamitos, CA, USA},
%doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.71},


@inproceedings{Peters2010aistats,
author = {J. Peters and D. Janzing and B. Sch\"{o}lkopf},
title = {Identifying Cause and Effect on Discrete Data using Additive Noise Models},
booktitle = {Proceedings of the 13th conference on Artificial Intelligence and Statistics ({AIStats})},
publisher = {Journal of Machine Learning Research: Workshop and Conference Proceedings},
volume = {9},
year = {2010},
pages = {597--604}
}



@inproceedings{Janzing2009uai,
title = {Identifying confounders using additive noise models},
author = {D. Janzing and J. Peters and J. M. Mooij and B. Sch\"{o}lkopf},
booktitle = {Proceedings of the 25th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI})},
publisher = {AUAI Press},
pages = {249--257},
year = {2009}
}
% address = {Corvallis, Oregon, USA},

@inproceedings{Mooij2009icml,
author = {J. M. Mooij and D. Janzing and J. Peters and B. Sch\"{o}lkopf},
booktitle = {Proceedings of the 26th International Conference on Machine Learning ({ICML})},
pages = {745--752},
title = {Regression by Dependence Minimization and its Application to Causal Inference},
publisher = {ACM Press},
year = {2009}
}

@inproceedings{Peters2009icml,
author = {J. Peters and D. Janzing and A. Gretton and B. Sch\"olkopf},
title = {Detecting the Direction of Causal Time Series},
booktitle = {Proceedings of the 26th International Conference on Machine Learning ({ICML})},
pages = {801--808},
publisher = {ACM Press},
year = {2009}
}

@inproceedings{Hoyer2009nips,
author = {P. O. Hoyer and D. Janzing and J. M. Mooij and J. Peters and B. Sch\"olkopf},
title = {Nonlinear causal discovery with additive noise models},
booktitle = {{A}dvances in {N}eural {I}nformation {P}rocessing {S}ystems 21 ({NeurIPS})},
pages = {689--696},
publisher = {Curran Associates, Inc.},
year = {2009}
}



@inproceedings{Peters2009gfkl,
author = {J. Peters and D. Janzing and A. Gretton and B. Sch{\"o}lkopf},
title = {Kernel methods for detecting the direction of time series},
booktitle = {Proccedings of the 32nd Annual Conference of the German Classification Society (GfKl 2008)},
year = {2009},
pages = {1--10}
}

@PhDThesis{Peters2012phd,
author = {J. Peters}, 
title = {Restricted Structural Equation Models for Causal Inference},
School = {ETH Zurich and MPI for Intelligent Systems},
year = {2012}
}
%note = {\url{http://dx.doi.org/10.3929/ethz-a-007597940}}
%doi = {10.3929/ethz-a-007597940}
%note = {\url{http://dx.doi.org/10.3929/ethz-a-007597940}}


@misc{Peters2008diploma,
author = {J. Peters}, 
title = {Asymmetries of Time Series under Inverting their Direction},
howpublished = {Diploma Thesis, University of Heidelberg},
year = {2008}
}
%note = {\url{http://web.math.ku.dk/~peters/publications.html}}










