% Econometrics VAR
@article{grabowski2020tobit,
  title={The Tobit cointegrated vector autoregressive model: An application to the currency market},
  author={Grabowski, Wojciech and Welfe, Aleksander},
  journal={Economic Modelling},
  volume={89},
  pages={88--100},
  year={2020},
  publisher={Elsevier}
}

% Finance VAR
@article{zivot2006vector,
  title={Vector autoregressive models for multivariate time series},
  author={Zivot, Eric and Wang, Jiahui},
  journal={Modeling Financial Time Series with S-Plus{\textregistered}},
  pages={385--429},
  year={2006},
  publisher={Springer}
}

@article{hyvarinen2010estimation,
  title={Estimation of a structural vector autoregression model using non-gaussianity.},
  author={Hyv{\"a}rinen, Aapo and Zhang, Kun and Shimizu, Shohei and Hoyer, Patrik O},
  journal={Journal of Machine Learning Research},
  volume={11},
  number={5},
  year={2010}
}


% Econometric VAR
@article{lutkepohl2009econometric,
  title={Econometric analysis with vector autoregressive models},
  author={L{\"u}tkepohl, Helmut},
  journal={Handbook of computational econometrics},
  pages={281--319},
  year={2009},
  publisher={John Wiley \& Sons}
}

% Neuroscience applications for VAR
@article{valdes2005estimating,
  title={Estimating brain functional connectivity with sparse multivariate autoregression},
  author={Vald{\'e}s-Sosa, Pedro A and S{\'a}nchez-Bornot, Jose M and Lage-Castellanos, Agust{\'i}n and Vega-Hern{\'a}ndez, Mayrim and Bosch-Bayard, Jorge and Melie-Garc{\'i}a, Lester and Canales-Rodr{\'i}guez, Erick},
  journal={Philosophical Transactions of the Royal Society B: Biological Sciences},
  volume={360},
  number={1457},
  pages={969--981},
  year={2005},
  publisher={The Royal Society London}
}

% Schur polynomials 
@book{macdonald1998symmetric,
  title={Symmetric functions and Hall polynomials},
  author={Macdonald, Ian Grant},
  year={1998},
  publisher={Oxford university press}
}

% Gershgorin Theorem
@book{varga2010gervsgorin,
  title={Ger{\v{s}}gorin and his circles},
  author={Varga, Richard S},
  volume={36},
  year={2010},
  publisher={Springer Science \& Business Media}
}

% Short proof of Cauchy's interlacing theorem
@article{fisk2005very,
  title={A very short proof of Cauchy's interlace theorem for eigenvalues of Hermitian matrices},
  author={Fisk, Steve},
  journal={arXiv preprint math/0502408},
  year={2005}
}

@inproceedings{transformer,
 author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, \L ukasz and Polosukhin, Illia},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.},
 title = {Attention is All you Need},
 volume = {30},
 year = {2017}
}



@InProceedings{deepar,
  title = 	 {Probabilistic Forecasting with Spline Quantile Function RNNs},
  author =       {Gasthaus, Jan and Benidis, Konstantinos and Wang, Yuyang and Rangapuram, Syama Sundar and Salinas, David and Flunkert, Valentin and Januschowski, Tim},
  booktitle = 	 {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics},
  pages = 	 {1901--1910},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Sugiyama, Masashi},
  volume = 	 {89},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {4},
  publisher =    {PMLR}
}


@misc{wavenet,
      title={WaveNet: A Generative Model for Raw Audio}, 
      author={Aaron van den Oord and Sander Dieleman and Heiga Zen and Karen Simonyan and Oriol Vinyals and Alex Graves and Nal Kalchbrenner and Andrew Senior and Koray Kavukcuoglu},
      year={2016},
      eprint={1609.03499},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}




@misc{gluonts,
      title={GluonTS: Probabilistic Time Series Models in Python}, 
      author={Alexander Alexandrov and Konstantinos Benidis and Michael Bohlke-Schneider and Valentin Flunkert and Jan Gasthaus and Tim Januschowski and Danielle C. Maddix and Syama Rangapuram and David Salinas and Jasper Schulz and Lorenzo Stella and Ali Caner Türkmen and Yuyang Wang},
      year={2019},
      eprint={1906.05264},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}


@misc{UCI,
author = "Dua, Dheeru and Graff, Casey",
year = "2017",
title = "UCI Machine Learning Repository",
url = "http://archive.ics.uci.edu/ml",
institution = "University of California, Irvine, School of Information and Computer Sciences" } 

% Introduction to Schur polynomials
@article{prasad2018introduction,
  title={An introduction to Schur polynomials},
  author={Prasad, Amritanshu},
  journal={arXiv preprint arXiv:1802.06073},
  year={2018}
}

% Explicit inverse
@article{el2003explicit,
  title={Explicit inverse of a generalized Vandermonde matrix},
  author={El-Mikkawy, Moawwad EA},
  journal={Applied mathematics and computation},
  volume={146},
  number={2-3},
  pages={643--651},
  year={2003},
  publisher={Elsevier}
}


% Polynomials schur
@article{de2001polynomials,
  title={Polynomials arising in factoring generalized Vandermonde determinants: an algorithm for computing their coefficients},
  author={De Marchi, Stefano},
  journal={Mathematical and computer modelling},
  volume={34},
  number={3-4},
  pages={271--281},
  year={2001},
  publisher={Elsevier}
}

% Matrix analysis
@book{horn2012matrix,
  title={Matrix analysis},
  author={Horn, Roger A and Johnson, Charles R},
  year={2012},
  publisher={Cambridge university press}
}

% Mixing props
@article{mokkadem1988mixing,
  title={Mixing properties of ARMA processes},
  author={Mokkadem, Abdelkader},
  journal={Stochastic processes and their applications},
  volume={29},
  number={2},
  pages={309--315},
  year={1988},
  publisher={Elsevier}
}

% Companion matrix and properties
@article{brand1964companion,
  title={The companion matrix and its properties},
  author={Brand, Louis},
  journal={The American Mathematical Monthly},
  volume={71},
  number={6},
  pages={629--634},
  year={1964},
  publisher={Taylor \& Francis}
}


% Some relevant lit in Estimating treatment effects
@article{hahn2001identification,
  title={Identification and estimation of treatment effects with a regression-discontinuity design},
  author={Hahn, Jinyong and Todd, Petra and Van der Klaauw, Wilbert},
  journal={Econometrica},
  volume={69},
  number={1},
  pages={201--209},
  year={2001},
  publisher={JSTOR}
}

@article{hill2006interval,
  title={Interval estimation for treatment effects using propensity score matching},
  author={Hill, Jennifer and Reiter, Jerome P},
  journal={Statistics in medicine},
  volume={25},
  number={13},
  pages={2230--2256},
  year={2006},
  publisher={Wiley Online Library}
}

@article{shi2019adapting,
  title={Adapting neural networks for the estimation of treatment effects},
  author={Shi, Claudia and Blei, David M and Veitch, Victor},
  journal={arXiv preprint arXiv:1906.02120},
  year={2019}
}


% Generalization bounds for CATE - Uri Shalit
@article{johansson2020generalization,
  title={Generalization bounds and representation learning for estimation of potential outcomes and causal effects},
  author={Johansson, Fredrik D and Shalit, Uri and Kallus, Nathan and Sontag, David},
  journal={arXiv preprint arXiv:2001.07426},
  year={2020}
}

% Generalization bounds for ITE - Uri Shalit
@inproceedings{shalit2017estimating,
  title={Estimating individual treatment effect: generalization bounds and algorithms},
  author={Shalit, Uri and Johansson, Fredrik D and Sontag, David},
  booktitle={International Conference on Machine Learning},
  pages={3076--3085},
  year={2017},
  organization={PMLR}
}


% Elastic Net
@article{zou2005regularization,
  title={Regularization and variable selection via the elastic net},
  author={Zou, Hui and Hastie, Trevor},
  journal={Journal of the royal statistical society: series B (statistical methodology)},
  volume={67},
  number={2},
  pages={301--320},
  year={2005},
  publisher={Wiley Online Library}
}


%Reference to LTI
@article{davison1976robust,
  title={The robust control of a servomechanism problem for linear time-invariant multivariable systems},
  author={Davison, Edward},
  journal={IEEE transactions on Automatic Control},
  volume={21},
  number={1},
  pages={25--34},
  year={1976},
  publisher={IEEE}
}

% forecasting


@article{Faloutsos2018, 
author = {Faloutsos, Christos and Gasthaus, Jan and Januschowski, Tim and Wang, Yuyang}, 
title = {Forecasting Big Time Series: Old and New}, 
year = {2018}, 
issue_date = {August 2018}, 
publisher = {VLDB Endowment}, 
volume = {11}, 
number = {12},
issn = {2150-8097}, 
numpages = {4} 
}


@article{Salinas2021,
title = {DeepAR: Probabilistic forecasting with autoregressive recurrent networks},
journal = {International Journal of Forecasting},
volume = {36},
number = {3},
pages = {1181-1191},
year = {2020},
issn = {0169-2070},
author = {David Salinas and Valentin Flunkert and Jan Gasthaus and Tim Januschowski},
}

@article{Januschowski2020,
title = {Criteria for classifying forecasting methods},
journal = {International Journal of Forecasting},
volume = {36},
number = {1},
pages = {167-177},
year = {2020},
note = {M4 Competition},
issn = {0169-2070},
author = {Tim Januschowski and Jan Gasthaus and Yuyang Wang and David Salinas and Valentin Flunkert and Michael Bohlke-Schneider and Laurent Callot},
}


@Article{Nauta2019,
AUTHOR = {Nauta, Meike and Bucur, Doina and Seifert, Christin},
TITLE = {Causal Discovery with Attention-Based Convolutional Neural Networks},
JOURNAL = {Machine Learning and Knowledge Extraction},
VOLUME = {1},
YEAR = {2019},
NUMBER = {1},
PAGES = {312--340},
}


% Granger causality 

@Article{Granger1969,
  author={Granger, Cleve},
  title={Investigating Causal Relations by Econometric Models and Cross-Spectral Methods},
  journal={Econometrica},
  year=1969,
  volume={37},
  number={3},
  pages={424-38},
  month={July},
  keywords={},
  abstract={},
}



% explainability 


@misc{wang2020,
	title={Shapley Flow: A Graph-based Approach to Interpreting Model Predictions}, 
	author={Jiaxuan Wang and Jenna Wiens and Scott Lundberg},
	year={2020},
	howpublished={arxiv:2010.14592},
	archivePrefix={arXiv},
	primaryClass={cs.LG}
}


@incollection{Lundberg2017,
title = {A Unified Approach to Interpreting Model Predictions},
author = {Lundberg, S. and Lee, S.},
booktitle = {Advances in Neural Information Processing Systems 30},
editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {4765--4774},
year = {2017},
publisher = {Curran Associates, Inc.},
}

@book{Molnar2019,
title = {Interpretable Machine Learning},
author = {Molnar, C.},
url = {https://christophm.github.io/interpretable-ml-book/},
publisher = {Molnar, C.},
year = {2019}
}


@InProceedings{janzing2020feature,
  title = 	 {Feature relevance quantification in explainable AI: A causal problem},
  author = 	 {Janzing, D. and Minorics, L. and Bloebaum, P.},
  booktitle = 	 {Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics},
  pages = 	 {2907--2916},
  year = 	 {2020},
  editor = 	 {Chiappa, S. and Calandra, R.},
  volume = 	 {108},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Online},
  month = 	 {8},
  publisher = 	 {PMLR}
}

% covariate shift
@book{Masashi2012, 
author = {Sugiyama, Masashi and Kawanabe, Motoaki}, 
title = {Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation}, 
year = {2012}, 
isbn = {0262017091}, 
publisher = {The MIT Press}, 
 }
 
 
% soft interventions

 @inproceedings{Markowetz2005,
  title={Probabilistic Soft Interventions in Conditional Gaussian Networks},
  author={Markowetz, F.  and Grossmann, S. and Spang, R.},
  booktitle={AISTATS},
  year={2005}
}


@article{Eberhardt2007,
author = {Eberhardt, F. and Scheines, R.},
title = {Interventions and Causal Inference},
journal = {Philosophy of Science},
volume = {74},
number = {5},
pages = {981-995},
year = {2007}
}

@article{CIC2020,
  author    = {Janzing, Dominik  and
                Bl{\"{o}}baum, Patrick and
                Minorics, Lenon and Faller,Philipp},
  title     = {Quantifying causal contribution via structure preserving interventions},
  year      = {2020},
  url       = {https://arxiv.org/abs/2007.00714},
  archivePrefix = {arXiv},
}



% Elements of causal inference book
@book{peters2017elements,
  title={Elements of causal inference},
  author={Peters, Jonas and Janzing, Dominik and Sch{\"o}lkopf, Bernhard},
  year={2017},
  publisher={The MIT Press}
}



% Spectral characterization of RKHS
@book{wendland2004scattered,
  title={Scattered data approximation},
  author={Wendland, Holger},
  volume={17},
  year={2004},
  publisher={Cambridge university press}
}

% Spectral characterization of RKHS
@article{kimeldorf1970correspondence,
  title = {A correspondence between Bayesian estimation on stochastic processes and smoothing by splines},
  author = {Kimeldorf, George S and Wahba, Grace},
  journal = {The Annals of Mathematical Statistics},
  volume = {41},
  number = {2},
  pages = {495--502},
  year = {1970},
  link = {http://www.jstor.org/stable/pdf/2239347.pdf},
  notes = {This seminal paper points out that splines can be interpreted as the posterior mean functions of certain Gaussian processes. The paper does not explicitly make the connection to the use of splines in numerics -- which means that many classic numerical methods in various areas can be interpreted as average-case Gaussian inference rules -- but later authors, most notably Diaconis, make it clear that they got the idea.}
}

% Pearl's book on Causality
@book{pearl2009causality,
  title={Causality},
  author={Pearl, Judea},
  year={2009},
  publisher={Cambridge university press}
}

% causal discovery 
@book{Spirtes1993,
  title = 			{Causation, Prediction, and Search},
  publisher = 	{Springer-Verlag},
  address = 		{New York, NY},
  year = 				{1993},
  author = 			{P. Spirtes and C. Glymour and R. Scheines},
}


% Brockwell and Davis book on Time series
@book{brockwell1991time,
  title={Time series: theory and methods: theory and methods},
  author={Brockwell, Peter J and Davis, Richard A and Fienberg, Stephen E},
  year={1991},
  publisher={Springer Science \& Business Media}
}

% Basu and Michalidis
@article{basu2015regularized,
  title={Regularized estimation in sparse high-dimensional time series models},
  author={Basu, Sumanta and Michailidis, George},
  journal={The Annals of Statistics},
  volume={43},
  number={4},
  pages={1535--1567},
  year={2015},
  publisher={Institute of Mathematical Statistics}
}

% VAR processes
@incollection{lutkepohl2013vector,
  title={Vector autoregressive models},
  author={L{\"u}tkepohl, Helmut},
  booktitle={Handbook of Research Methods and Applications in Empirical Macroeconomics},
  year={2013},
  publisher={Edward Elgar Publishing}
}

% VC bounds, blocking technique
@article{yu1994rates,
  title={Rates of convergence for empirical processes of stationary mixing sequences},
  author={Yu, Bin},
  journal={The Annals of Probability},
  pages={94--116},
  year={1994},
  publisher={JSTOR}
}

% VC bounds 
@article{meir2000nonparametric,
  title={Nonparametric time series prediction through adaptive model selection},
  author={Meir, Ron},
  journal={Machine learning},
  volume={39},
  number={1},
  pages={5--34},
  year={2000},
  publisher={Springer}
}

%Stability bounds Mohri
@article{mohri2010stability,
  title={Stability Bounds for Stationary $\varphi$-mixing and $\beta$-mixing Processes.},
  author={Mohri, Mehryar and Rostamizadeh, Afshin},
  journal={Journal of Machine Learning Research},
  volume={11},
  number={2},
  year={2010}
}

% Mohri Rademacher Mixing
@inproceedings{mohriRademacher,
 author = {Mohri, Mehryar and Rostamizadeh, Afshin},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {D. Koller and D. Schuurmans and Y. Bengio and L. Bottou},
 publisher = {Curran Associates, Inc.},
 title = {Rademacher Complexity Bounds for Non-I.I.D. Processes},
 volume = {21},
 year = {2009}
}

% Mcdonald Rademacher Mixing
@article{mcdonald2011generalization,
  title={Generalization error bounds for stationary autoregressive models},
  author={McDonald, Daniel J and Shalizi, Cosma Rohilla and Schervish, Mark},
  journal={arXiv preprint arXiv:1103.0942},
  year={2011}
}


@article{mcdonald2017nonparametric,
  title={Nonparametric risk bounds for time-series forecasting},
  author={McDonald, Daniel J and Shalizi, Cosma Rohilla and Schervish, Mark},
  journal={The Journal of Machine Learning Research},
  volume={18},
  number={1},
  pages={1044--1083},
  year={2017},
  publisher={JMLR.org}
}


@article{mohri2010stability,
  title={Stability Bounds for Stationary $\varphi$-mixing and $\beta$-mixing Processes.},
  author={Mohri, Mehryar and Rostamizadeh, Afshin},
  journal={Journal of Machine Learning Research},
  volume={11},
  number={2},
  year={2010}
}


@inproceedings{janzingRegularization,
 author = {Janzing, Dominik},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
 publisher = {Curran Associates, Inc.},
 title = {Causal Regularization},
 volume = {32},
 year = {2019}
}


@inproceedings{melnyk2016estimating,
  title={Estimating structured vector autoregressive models},
  author={Melnyk, Igor and Banerjee, Arindam},
  booktitle={International Conference on Machine Learning},
  pages={830--839},
  year={2016},
  organization={PMLR}
}


@article {bernstein1927extension,
  title = {On the extension of the th {\ 'e} or {\ `e} me limit of the calculation of probabilities {\' e} s to the pending sums of quantity},
  author = {Bernstein, Serge},
  journal = {Mathematische Annalen},
  volume = {97},
  number = {1},
  pages = {1--59},
  year = {1927},
  publisher = {Springer}
}


@article{tamvakis2012theory,
  title={The theory of Schur polynomials revisited},
  author={Tamvakis, Harry},
  journal={L’Enseignement Mathematique},
  volume={58},
  number={1},
  pages={147--163},
  year={2012}
}


@article{chaugule2019schur,
  title={Schur Polynomials do not have small formulas if the Determinant doesn't!},
  author={Chaugule, Prasad and Kumar, Mrinal and Limaye, Nutan and Mohapatra, Chandra Kanta and She, Adrian and Srinivasan, Srikanth},
  journal={arXiv preprint arXiv:1911.12520},
  year={2019}
}

@article {cauchy1815memoire,
  title = {M {\ 'e} memory on functions which can obtain only two equal values {\' e} and of opposite signs as a result of the transpositions op {\ 'e} r {\' e} es between the variables that they contain},
  author = {Cauchy, Augustin Louis},
  journal = {Journal de l'Ecole polytechnique},
  volume = {10},
  number = {17},
  pages = {29--112},
  year = {1815}
}

@book{macdonald1998symmetric,
  title={Symmetric functions and Hall polynomials},
  author={Macdonald, Ian Grant},
  year={1998},
  publisher={Oxford university press}
}

@article{jacobi1841functionibus,
  title={De functionibus alternantibus earumque divisione per productum e differentiis elementorum conflatum.},
  author={Jacobi, Carl Gustav Jakob},
  journal={Journal f{\"u}r die reine und angewandte Mathematik (Crelles Journal)},
  volume={1841},
  number={22},
  pages={360--371},
  year={1841},
  publisher={De Gruyter}
}


@incollection{vapnik2015uniform,
  title={On the uniform convergence of relative frequencies of events to their probabilities},
  author={Vapnik, Vladimir N and Chervonenkis, A Ya},
  booktitle={Measures of complexity},
  pages={11--30},
  year={2015},
  publisher={Springer}
}

@inproceedings{boser1992training,
  title={A training algorithm for optimal margin classifiers},
  author={Boser, Bernhard E and Guyon, Isabelle M and Vapnik, Vladimir N},
  booktitle={Proceedings of the fifth annual workshop on Computational learning theory},
  pages={144--152},
  year={1992}
}

@article{mansour2009domain,
  title={Domain adaptation: Learning bounds and algorithms},
  author={Mansour, Yishay and Mohri, Mehryar and Rostamizadeh, Afshin},
  journal={arXiv preprint arXiv:0902.3430},
  year={2009}
}

@article{ben2007analysis,
  title={Analysis of representations for domain adaptation},
  author={Ben-David, Shai and Blitzer, John and Crammer, Koby and Pereira, Fernando and others},
  journal={Advances in neural information processing systems},
  volume={19},
  pages={137},
  year={2007},
  publisher={MIT; 1998}
}

@article{bartlett2002rademacher,
  title={Rademacher and Gaussian complexities: Risk bounds and structural results},
  author={Bartlett, Peter L and Mendelson, Shahar},
  journal={Journal of Machine Learning Research},
  volume={3},
  number={Nov},
  pages={463--482},
  year={2002}
}

% invariant prediction
@article{pfister2019invariant,
title={Invariant causal prediction for sequential data},
author={Pfister, Niklas and B{\"u}hlmann, Peter and Peters, Jonas}, journal={Journal of the American Statistical Association}, volume={114},
number={527},
pages={1264--1276}, year={2019}, publisher={Taylor \& Francis} }

% reinforcement learning
@inproceedings{bennett2021off,
title={Off-policy evaluation in infinite-horizon reinforcement learning with latent confounders}, author={Bennett, Andrew and Kallus, Nathan and Li, Lihong and Mousavi, Ali}, booktitle={International Conference on Artificial Intelligence and Statistics}, pages={1999--2007},
year={2021},
organization={PMLR}
}

% imitation learning
@article{zhang2020causal,
title={Causal imitation learning with unobserved confounders},
author={Zhang, Junzhe and Kumor, Daniel and Bareinboim, Elias},
journal={Advances in neural information processing systems},
volume={33},
year={2020}
}

@inproceedings{bica20,
  title = {Estimating counterfactual treatment outcomes over time through adversarially balanced representations},
  author = {Ioana Bica and Ahmed M. Alaa and James Jordon and Mihaela van der Schaar},
  year = {2020},
  url = {https://openreview.net/forum?id=BJg866NFvB},
  researchr = {https://researchr.org/publication/BicaAJS20},
  cites = {0},
  citedby = {0},
  booktitle = {8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020},
  publisher = {OpenReview.net},
}

@inproceedings{Lim2018, 
author = {Lim, Bryan and Alaa, Ahmed and Schaar, Mihaela van der}, 
title = {Forecasting Treatment Responses over Time Using Recurrent Marginal Structural Networks}, 
year = {2018}, 
publisher = {Curran Associates Inc.}, 
address = {Red Hook, NY, USA}, 
booktitle = {Proceedings of the 32nd International Conference on Neural Information Processing Systems}, 
pages = {7494–7504}, 
numpages = {11}, 
location = {Montr\'{e}al, Canada}, 
series = {NIPS'18} }

% non causal generalization bounds
@article{McDonald2017, 
author = {McDonald, Daniel J. and Shalizi, Cosma Rohilla and Schervish, Mark}, 
title = {Nonparametric Risk Bounds for Time-Series Forecasting}, year = {2017}, issue_date = {January 2017}, 
publisher = {JMLR.org}, 
volume = {18}, 
number ={1},  
journal = {J. Mach. Learn. Res.}, 
month = {1}, 
pages = {1044–1083}, 
numpages = {40}, 
keywords = {model selection, linear time-invariant systems, VC dimension, statespace models, prediction risk, generalization error} }

@article{m4,
author = {Makridakis, Spyros and Spiliotis, Evangelos and Assimakopoulos, Vassilis},
year = {2018},
month = {06},
pages = {},
title = {The M4 Competition: Results, findings, conclusion and way forward},
volume = {34},
journal = {International Journal of Forecasting},
doi = {10.1016/j.ijforecast.2018.06.001}
}

@misc{mcdonald_2018, 
title={Generalization Error Bounds for Time Series}, 
}, 
publisher={Carnegie Mellon University}, 
author={McDonald, Daniel J.}, 
year={2018}, 
month={Jul} } 

% sequential deconfounding
@misc{hatt2021sequential,
      title={Sequential Deconfounding for Causal Inference with Unobserved Confounders}, 
      author={Tobias Hatt and Stefan Feuerriegel},
      year={2021},
      eprint={2104.09323},
      archivePrefix={arXiv},
      primaryClass={stat.ME}
}

% anchor regression
@ARTICLE{Rothenhaeusler2021,
       author = {{Rothenh{\"a}usler}, D. and {Meinshausen}, N. and
         {B{\"u}hlmann}, P. and {Peters}, J.},
        title = "{Anchor regression: heterogeneous data meets causality}",
      journal = {Journal Royal Statistical Society Series B},
         year = "2021",
         number = {83},
         pages = {215--246} 
} 


@inproceedings{Jin2021,
  title={Is Pessimism Provably Efficient for Offline RL?},
  author={Ying-hua Jin and Zhuoran Yang and Zhaoran Wang},
  booktitle={ICML},
  year={2021}
}