% Uhler lab
@inproceedings{wang2018,
 author = {Wang, Yuhao and Squires, Chandler and Belyaeva, Anastasiya and Uhler, Caroline},
 booktitle = {Advances in Neural Information Processing Systems},
 title = {Direct Estimation of Differences in Causal Graphs},
 volume = {31},
 year = {2018}
}

% Candes lab
@article{Candes2018,
author = {Cand{\`{e}}s, Emmanuel and Fan, Yingying and Janson, Lucas and Lv, Jinchi},
journal = {J. R. Statist. Soc. B},
number = {3},
pages = {551--577},
title = {Panning for gold: ‘{M}odel-{X}' knockoffs for high dimensional controlled variable selection},
volume = {80},
year = {2018}
}
@article{Sesia2019,
author = {Sesia, Matteo and Sabatti, Chiara and Candès, Emmanuel J.},
title = {{Gene hunting with hidden Markov model knockoffs}},
journal = {Biometrika},
volume = {106},
number = {1},
pages = {1-18},
year = {2018}
}
@article{Bates2020,
author = {Bates, Stephen and Sesia, Matteo and Sabatti, Chiara and Cand{\`{e}}s, Emmanuel},
journal = {Proceedings of the National Academy of Sciences},
number = {39},
pages = {24117 LP  -- 24126},
title = {{Causal inference in genetic trio studies}},
volume = {117},
year = {2020}
}
@article{barber2015,
author = {Barber, Rina Foygel and Cand{\`{e}}s, Emmanuel J},
journal = {Ann. Statist.},
number = {5},
pages = {2055--2085},
title = {{Controlling the false discovery rate via knockoffs}},
volume = {43},
year = {2015}
}


% ETH Zurich
@article{Meinshausen2010,
author = {Meinshausen, Nicolai and B{\"{u}}hlmann, Peter},
journal = {J. R. Statist. Soc. B},
number = {4},
pages = {417--473},
title = {{Stability selection}},
volume = {72},
year = {2010}
}
@article{maathuis2009,
author = {Maathuis, Marloes H and Kalisch, Markus and B{\"{u}}hlmann, Peter},
journal = {Ann. Statist.},
number = {6A},
pages = {3133--3164},
title = {{Estimating high-dimensional intervention effects from observational data}},
volume = {37},
year = {2009}
}
@article{Kalisch2007,
author = {Kalisch, Markus and B{\"{u}}hlmann, Peter},
journal = {J. Mach. Learn. Res.},
pages = {613--636},
title = {{Estimating high-dimensional directed acyclic graphs with the PC-algorithm}},
volume = {8},
year = {2007}
}
@article{Meinshausen2016,
author = {Meinshausen, Nicolai and Hauser, Alain and Mooij, Joris M. and Peters, Jonas and Versteeg, Philip and B{\"{u}}hlmann, Peter},
journal = {Proceedings of the National Academy of Sciences},
number = {27},
pages = {7361--7368},
pmid = {27382150},
title = {{Methods for causal inference from gene perturbation experiments and validation}},
volume = {113},
year = {2016}
}
@article{Peters2016,
author = {Peters, Jonas and B{\"{u}}hlmann, Peter and Meinshausen, Nicolai},
issn = {1369-7412},
journal = {J. R. Statist. Soc. B)},
keywords = {Causal discovery,Causal inference,Confidence intervals,Invariant prediction},
number = {5},
pages = {947--1012},
title = {{Causal inference by using invariant prediction: identification and confidence intervals}},
volume = {78},
year = {2016}
}
@article{Buhlmann2020,
author = {B{\"{u}}hlmann, Peter},
journal = {Statist. Sci.},
keywords = {Anchor regression,Random Forests,causal regularization,distributional robustness,heterogeneous data,instrumental variables regression,interventional data,variable importance},
number = {3},
pages = {404--426},
title = {{Invariance, Causality and Robustness}},
volume = {35},
year = {2020}
}
@article{Stekhoven2012,
author = {Stekhoven, Daniel J and Moraes, Izabel and Sveinbj{\"{o}}rnsson, Gardar and Hennig, Lars and Maathuis, Marloes H and B{\"{u}}hlmann, Peter},
journal = {Bioinformatics},
number = {21},
pages = {2819--2823},
title = {{Causal stability ranking}},
volume = {28},
year = {2012}
}

% Entner and similar
@InProceedings{entner2013,
  title = 	 {Data-driven covariate selection for nonparametric estimation of causal effects},
  author = 	 {Entner, Doris and Hoyer, Patrik and Spirtes, Peter},
  booktitle = 	 {Proceedings of the 16th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {256--264},
  year = 	 {2013}
}
@inproceedings{magliacane2016,
author = {Magliacane, Sara and Claassen, Tom and Mooij, Joris M.},
title = {Ancestral Causal Inference},
year = {2016},
booktitle = {Advances in Neural Information Processing Systems 30},
pages = {4473–4481}
}
@inproceedings{Magliacane2018,
author = {Magliacane, Sara and van Ommen, Thijs and Claassen, Tom and Bongers, Stephan and Versteeg, Philip and Mooij, Joris M.},
title = {Domain adaptation by using causal inference to predict invariant conditional distributions},
year = {2018},
booktitle = {Advances in Neural Information Processing Systems 32},
pages = {10869–10879}
}
@InProceedings{gultchin2020,
  title = 	 {Differentiable causal backdoor discovery},
  author =       {Gultchin, Limor and Kusner, Matt and Kanade, Varun and Silva, Ricardo},
  booktitle = 	 {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics},
  pages = 	 {3970--3979},
  year = 	 {2020}
}
@inproceedings{Claassen2011,
author = {Claassen, Tom and Heskes, Tom},
booktitle = {Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence},
pages = {135--144},
title = {A Logical Characterization of Constraint-Based Causal Discovery},
year = {2011}
}
@inproceedings{Mooij2015,
author = {Mooij, Joris M and Cremers, Jerome},
booktitle = {Proceedings of the UAI 2015 Conference on Advances in Causal Inference},
pages = {30--39},
title = {An Empirical Study of One of the Simplest Causal Prediction Algorithms},
year = {2015}
}
@inproceedings{Hytinnen2014,
author = {Hyttinen, Antti and Eberhardt, Frederick and J\"{a}rvisalo, Matti},
title = {Constraint-Based Causal Discovery: Conflict Resolution with Answer Set Programming},
year = {2014},
booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence},
pages = {340–349}
}

% Benchmark
@article{mooij_benchmark,
author = {Mooij, Joris M and Peters, Jonas and Janzing, Dominik and Zscheischler, Jakob and Sch{\"{o}}lkopf, Bernhard},
journal = {J. Mach. Learn. Res.},
number = {32},
pages = {1--102},
title = {Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks},
volume = {17},
year = {2016}
}

% Best subset
@article{Bertsimas2016,
author = {Bertsimas, Dimitris and King, Angela and Mazumder, Rahul},
journal = {Ann. Statist.},
number = {2},
pages = {813--852},
title = {{Best subset selection via a modern optimization lens}},
volume = {44},
year = {2016}
}
@article{Bertsimas2020,
author = {Bertsimas, Dimitris and Parys, Bart Van},
journal = {Ann. Statist.},
number = {1},
pages = {300--323},
title = {{Sparse high-dimensional regression: Exact scalable algorithms and phase transitions}},
volume = {48},
year = {2020}
}
@article{Natarajan1995,
author = {Natarajan, B K},
journal = {SIAM J. Comput.},
number = {2},
pages = {227--234},
title = {Sparse Approximate Solutions to Linear Systems},
volume = {24},
year = {1995}
}

% Feature selection
@article{Guyon2003,
author = {Guyon, Isabelle and Elisseeff, Andr{\'{e}}},
journal = {J. Mach. Learn. Res.},
pages = {1157--1182},
title = {An Introduction to Variable and Feature Selection},
volume = {3},
year = {2003}
}

% Lasso
@article{Tibshirani1996,
author = {Tibshirani, Robert},
journal = {J. R. Statist. Soc. B},
number = {1},
pages = {267--288},
title = {Regression Shrinkage and Selection via the Lasso},
volume = {58},
year = {1996}
}
@article{Hastie2020,
author = {Hastie, Trevor and Tibshirani, Robert and Tibshirani, Ryan},
journal = {Statist. Sci.},
number = {4},
pages = {579--592},
title = {{Best subset, forward stepwise or lasso? Analysis and recommendations based on extensive comparisons}},
volume = {35},
year = {2020}
}
@article{Belloni2013,
author = {Belloni, Alexandre and Chernozhukov, Victor},
journal = {Bernoulli},
number = {2},
pages = {521--547},
title = {{Least squares after model selection in high-dimensional sparse models}},
volume = {19},
year = {2013}
}


% Forward selection

% Recursive feature elimination
@article{Guyon2002,
author = {Guyon, Isabelle and Weston, Jason and Barnhill, Stephen and Vapnik, Vladimir},
journal = {Mach. Learn.},
number = {1},
pages = {389--422},
title = {Gene Selection for Cancer Classification using Support Vector Machines},
volume = {46},
year = {2002}
}
@article{Gregorutti2017,
author = {Gregorutti, Baptiste and Michel, Bertrand and Saint-Pierre, Philippe},
journal = {Stat. Comput.},
number = {3},
pages = {659--678},
title = {{Correlation and variable importance in random forests}},
volume = {27},
year = {2017}
}


% Classics
@book{Pearl2009,
address = {New York},
author = {Pearl, Judea},
publisher = {Cambridge University Press},
title = {{Causality: Models, Reasoning, and Inference}},
edition={2nd},
year = {2009}
}
@book{Spirtes2000,
address = {Cambridge, MA},
author = {Spirtes, Peter and Glymour, Clark N. and Scheines, Richard},
edition = {2nd},
publisher = {The MIT Press},
title = {Causation, Prediction, and Search},
year = {2000}
}
@article{Dawid1979,
author = {Dawid, A P},
journal = {J. R. Statist. Soc. B},
number = {1},
pages = {1--31},
title = {Conditional Independence in Statistical Theory},
volume = {41},
year = {1979}
}


% LiNGAM
@article{shimizu2006_LiNGAM,
author = {Shimizu, Shohei and Hoyer, Patrik O. and Hyv\"{a}rinen, Aapo and Kerminen, Antti},
title = {A Linear Non-Gaussian Acyclic Model for Causal Discovery},
year = {2006},
volume = {7},
journal = {J. Mach. Learn. Res.},
pages = {2003–2030}
}
@article{shimizu2011_directLiNGAM,
author = {Shimizu, Shohei and Inazumi, Takanori and Sogawa, Yasuhiro and Hyv\"{a}rinen, Aapo and Kawahara, Yoshinobu and Washio, Takashi and Hoyer, Patrik O. and Bollen, Kenneth},
title = {{DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model}},
year = {2011},
volume = {12},
journal = {J. Mach. Learn. Res.},
pages = {1225–1248},
numpages = {24}
}

% Causal discovery overview
@article{Eberhardt2017,
author = {Eberhardt, Frederick},
issn = {2364-4168},
journal = {Int. J. Data Sci. Anal.},
number = {2},
pages = {81--91},
title = {{Introduction to the foundations of causal discovery}},
volume = {3},
year = {2017}
}
@article{glymour2019,
author = {Glymour, Clark and Zhang, Kun and Spirtes, Peter},
journal = {Front. Genet.},
pages = {524},
title = {Review of Causal Discovery Methods Based on Graphical Models},
volume = {10},
year = {2019}
}
@article{Mooij2020_jci,
  author  = {Joris M. Mooij and Sara Magliacane and Tom Claassen},
  title   = {Joint Causal Inference from Multiple Contexts},
  journal = {J. Mach. Learn. Res.},
  year    = {2020},
  volume  = {21},
  number  = {99},
  pages   = {1-108}
}

% FCI extra add ons (give completeness)
@inproceedings{Ali2005,
author = {R.A. Ali and T. Richardson and P. Spirtes and J. Zhang},
booktitle = {Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence},
pages = {10--17},
title = {{Towards characterizing Markov equivalence classes for directed acyclic graphs with latent variables}},
year = {2005}
}
@article{zhang_complete_fci,
title = {On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias},
journal = {Artif. Intell.},
volume = {172},
number = {16},
pages = {1873--1896},
year = {2008},
author = {Jiji Zhang}
}

% Classic methods
@article{Chickering2003,
author = {Chickering, David Maxwell},
title = {Optimal Structure Identification with Greedy Search},
year = {2003},
volume = {3},
journal = {J. Mach. Learn. Res.},
pages = {507–554},
numpages = {48}
}
@article{Colombo2012,
author = {Diego Colombo and Marloes H. Maathuis and Markus Kalisch and Thomas S. Richardson},
title = {{Learning high-dimensional directed acyclic graphs with latent and selection variables}},
volume = {40},
journal = {Ann. Statist.},
number = {1},
pages = {294 -- 321},
year = {2012}
}


% Challenges to faithfulness assumption
@article{Uhler2013,
author = {Uhler, Caroline and Raskutti, Garvesh and Buhlmann, Peter and Yu, Bin},
journal = {Ann. Statist.},
number = {2},
pages = {436--463},
title = {{Geometry of the faithfulness assumption in causal inference}},
volume = {41},
year = {2013}
}
@article{Steel2006,
author = {Steel, Daniel},
journal = {Minds Mach.},
number = {3},
pages = {303--317},
title = {{Homogeneity, selection, and the faithfulness condition}},
volume = {16},
year = {2006}
}

@article{zhang2008_unfaithful,
author = {Zhang, Jiji and Spirtes, Peter},
title = {Detection of Unfaithfulness and Robust Causal Inference},
year = {2008},
volume = {18},
number = {2},
journal = {Minds Mach.},
pages = {239–271},
numpages = {33}
}

@article{Andersen2013,
author = {Andersen, Holly},
journal = {Phil. Sci.},
number = {5},
pages = {672--683},
title = {When to Expect Violations of Causal Faithfulness and Why It Matters},
volume = {80},
year = {2013}
}
@article{Cartwright2001,
author = {Cartwright, Nancy},
journal = {Monist},
number = {2},
pages = {242--264},
title = {{What is wrong with Bayes nets?}},
volume = {84},
year = {2001}
}

@article{Forster2018,
author = {Forster, Malcolm and Raskutti, Garvesh and Stern, Reuben and Weinberger, Naftali},
title = {The Frugal Inference of Causal Relations},
journal = {Br. J. Philos. Sci.},
volume = {69},
number = {3},
pages = {821-848},
year = {2018}
}
@InProceedings{marx2021,
  title = 	 {A weaker faithfulness assumption based on triple interactions},
  author =       {Marx, Alexander and Gretton, Arthur and Mooij, Joris M.},
  booktitle = 	 {Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence},
  pages = 	 {451--460},
  year = 	 {2021}
}
@book{tmle2011,
address = {New York},
author = {van der Laan, Mark J. and Rose, Sherri},
publisher = {Springer},
title = {Targeted learning: Causal Inference for Observational and Experimental Data},
year = {2011}
}
@article{rosenbaum_1983,
author = {Rosenbaum, Paul R. and Rubin, Donald B.},
journal = {Biometrika},
number = {1},
pages = {41--55},
title = {{The central role of the propensity score in observational studies for causal effects}},
volume = {70},
year = {1983}
}


% On conditional independence
@book{Lehmann2005,
address = {New York},
author = {Lehmann, E.L. and Romano, Joseph P.},
edition = {{Third}},
publisher = {Springer},
title = {{Testing Statistical Hypotheses}},
year = {2005}
}
@article{shah2020,
author = {Rajen D. Shah and Jonas Peters},
title = {{The hardness of conditional independence testing and the generalised covariance measure}},
volume = {48},
journal = {Ann. Statist.},
number = {3},
pages = {1514 -- 1538},
year = {2020}
}
@article{lei2018,
author = {Jing Lei and Max G’Sell and Alessandro Rinaldo and Ryan J. Tibshirani and Larry Wasserman},
title = {Distribution-Free Predictive Inference for Regression},
journal = {J. Am. Stat. Assoc.},
volume = {113},
number = {523},
pages = {1094-1111},
year  = {2018}
}

% Requisite
@article{shpitser2008,
author = {Shpitser, Ilya and Pearl, Judea},
issn = {1532-4435},
journal = {J. Mach. Learn. Res.},
month = {jun},
pages = {1941--1979},
publisher = {JMLR.org},
title = {Complete Identification Methods for the Causal Hierarchy},
volume = {9},
year = {2008}
}

% Self-call
@article{Watson2021,
author = {Watson, David S and Wright, Marvin N},
journal = {Mach. Learn.},
title = {{Testing conditional independence in supervised learning algorithms}},
volue = {110},
year = {2021}
}

% Uniform v. pointwise
@article{Robins2003,
author = {Robins, James M. and Sheines, R. and Spirtes, P. and Wasserman, L.},
journal = {Biometrika},
number = {3},
pages = {491--515},
title = {{Uniform consistency in causal inference}},
volume = {90},
year = {2003}
}

@article{silva2016,
author = {Silva, Ricardo and Evans, Robin},
journal = {J. Mach. Learn. Res.},
number = {56},
pages = {1--53},
title = {Causal Inference through a Witness Protection Program},
volume = {17},
year = {2016}
}

@article{Wang2019,
author = {Wang, Yixin and Blei, David M},
journal = {J. Am. Stat. Assoc.},
number = {528},
pages = {1574--1596},
title = {The Blessings of Multiple Causes},
volume = {114},
year = {2019}
}

% FDR
@article{Benjamini1995,
author = {Benjamini, Yoav and Hochberg, Yosef},
journal = {J. R. Statist. Soc. B},
number = {1},
pages = {289--300},
title = {Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing},
volume = {57},
year = {1995}
}
@article{Storey2007,
    author = {Storey, John D},
    journal = {J. R. Statist. Soc. B},
    number = {3},
    pages = {347--368},
    title = {The Optimal Discovery Procedure: A New Approach to Simultaneous Significance Testing},
    volume = {69},
    year = {2007}
}
@article{Storey2002,
author = {Storey, John D},
journal = {J. R. Statist. Soc. B},
number = {3},
pages = {479--498},
publisher = {[Royal Statistical Society, Wiley]},
title = {A Direct Approach to False Discovery Rates},
volume = {64},
year = {2002}
}
@article{Storey2003,
author = {Storey, John D. and Tibshirani, Robert},
journal = {Proc. Natl. Acad. Sci. },
number = {16},
pages = {9440--9445},
title = {{Statistical significance for genomewide studies}},
volume = {100},
year = {2003}
}
@article{Lei2018_adapt,
author = {Lei, Lihua and Fithian, William},
issn = {1369-7412},
journal = {J. R. Statist. Soc. B},
number = {4},
pages = {649--679},
title = {{AdaPT: an interactive procedure for multiple testing with side information}},
volume = {80},
year = {2018}
}
@article{Ignatiadis2016,
author = {Ignatiadis, Nikolaos and Klaus, Bernd and Zaugg, Judith B and Huber, Wolfgang},
journal = {Nat. Meth.},
number = {7},
pages = {577--580},
title = {{Data-driven hypothesis weighting increases detection power in genome-scale multiple testing}},
volume = {13},
year = {2016}
}
@article{Shah2013,
author = {Shah, Rajen D. and Samworth, Richard J.},
journal = {J. R. Statist. Soc. B},
number = {1},
pages = {55--80},
title = {{Variable selection with error control: Another look at stability selection}},
volume = {75},
year = {2013}
}

% Graphs et al.
@article{bongers2021,
author = {Stephan Bongers and Patrick Forré and Jonas Peters and Joris M. Mooij},
title = {{Foundations of structural causal models with cycles and latent variables}},
volume = {49},
journal = {Ann. Statist.},
number = {5},
pages = {2885 -- 2915},
year = {2021}
}

@inproceedings{pearl1995testability,
  title={On the testability of causal models with latent and instrumental variables},
  author={Pearl, Judea},
  booktitle={Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence},
  pages={435--443},
  year={1995}
}

% HSIC
@inproceedings{Zhang2011,
author = {Zhang, Kun and Peters, Jonas and Janzing, Dominik and Sch\"{o}lkopf, Bernhard},
title = {Kernel-Based Conditional Independence Test and Application in Causal Discovery},
year = {2011},
booktitle = {Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence},
pages = {804–813}
}

@article{Leduc2012,
author = {Leduc, Magalie S and Blair, Rachael Hageman and Verdugo, Ricardo A and Tsaih, Shirng-Wern and Walsh, Kenneth and Churchill, Gary A and Paigen, Beverly},
journal = {J. Lipid Research},
number = {6},
pages = {1163--1175},
title = {{Using bioinformatics and systems genetics to dissect HDL-cholesterol genetics in an MRL/MpJ x SM/J intercross.}},
volume = {53},
year = {2012}
}

@book{Hernan2020,
author = {Hern\'{a}n, Miguel A and Robins, James M},
address = {Boca Raton},
publisher = {Chapman \& Hall/CRC},
title = {{Causal Inference: What If}},
year = {2009}
}
@phdthesis{Meek1997,
    title    = {Graphical Models: Selecting Causal and Statistical Models},
    school   = {Carnegie Mellon University},
    author   = {Meek, Christopher},
    year     = {1997}
}
@article{Wang2019,
author = {Wang, Yixin and Blei, David M},
journal = {J. Am. Stat. Assoc.},
number = {528},
pages = {1574--1596},
title = {The Blessings of Multiple Causes},
volume = {114},
year = {2019}
}
@article{Pearl2009M,
author = {Pearl, Judea},
journal = {UCAL Cognitive Systems Lab, Technical Report TR-348},
title = {Myth, confusion, and science in causal analysis},
year = {2009}
}
@article{Ding2015,
author = {Ding, Peng and Miratrix, Luke},
journal = {J. Causal Inference},
number = {1},
pages = {41--57},
title = {To Adjust or Not to Adjust? {S}ensitivity Analysis of {M}-Bias and Butterfly-Bias},
volume = {3},
year = {2015}
}
@article{richardson2003,
author = {Richardson, Thomas},
journal = {Scand. J. Stat.},
number = {1},
pages = {145--157},
title = {Markov Properties for Acyclic Directed Mixed Graphs},
volume = {30},
year = {2003}
}
@article{colombo2014,
  author  = {Diego Colombo and Marloes H. Maathuis},
  title   = {Order-Independent Constraint-Based Causal Structure Learning},
  journal = {J. Mach. Learn. Res.},
  year    = {2014},
  volume  = {15},
  number  = {116},
  pages   = {3921-3962}
}
@inproceedings{mani2006,
  author  = {Mani, Subramani and Spirtes, Peter and Cooper, Gregory},
  title   = {A theoretical study of {Y} structures for causal discovery},
  booktitle = {Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence},
  year    = {2006},
  pages   = {314--323}
}


@article{Breiman2001,
author = {Breiman, Leo},
journal = {Mach. Learn.},
number = {1},
pages = {1--33},
title = {Random forests},
volume = {45},
year = {2001}
}
@article{buhlmann_l2boost,
author = {Peter Bühlmann and Bin Yu},
title = {{Boosting with the $L_2$ loss}},
journal = {J. Am. Stat. Assoc.},
volume = {98},
number = {462},
pages = {324-339},
year  = {2003}
}
@article{friedman_gbm,
author = {Jerome H. Friedman},
title = {{Greedy function approximation: A gradient boosting machine}},
volume = {29},
journal = {Ann. Statist.},
number = {5},
pages = {1189 -- 1232},
year = {2001}
}

% Packages
@article{glmnet_pkg,
 title={Regularization Paths for Generalized Linear Models via Coordinate Descent},
 volume={33},
 number={1},
 journal={J. Stat. Softw.},
 author={Friedman, Jerome H. and Hastie, Trevor and Tibshirani, Rob},
 year={2010},
 pages={1–22}
}
@inproceedings{lightgbm_pkg,
 author = {Ke, Guolin and Meng, Qi and Finley, Thomas and Wang, Taifeng and Chen, Wei and Ma, Weidong and Ye, Qiwei and Liu, Tie-Yan},
 booktitle = {Advances in Neural Information Processing Systems},
 title = {LightGBM: A Highly Efficient Gradient Boosting Decision Tree},
 volume = {30},
 year = {2017}
}
@article{pcalg,
 title={Causal Inference Using Graphical Models with the {R} Package pcalg},
 volume={47},
 number={11},
 journal={J. Stat. Softw.},
 author={Kalisch, Markus and Mächler, Martin and Colombo, Diego and Maathuis, Marloes H. and Bühlmann, Peter},
 year={2012},
 pages={1–26}
}

% Causal inference methods
@article{hahn_bart,
author = {P. Richard Hahn and Jared S. Murray and Carlos M. Carvalho},
title = {Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion)},
volume = {15},
journal = {Bayesian Anal.},
number = {3},
pages = {965 -- 2020},
year = {2020}
}
@article{Athey2019,
author = {Athey, Susan and Tibshirani, Julie and Wager, Stefan},
journal = {Ann. Statist.},
number = {2},
pages = {1148--1178},
title = {{Generalized random forests}},
volume = {47},
year = {2019}
}
@article{Chernozhukov2018,
author = {Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Duflo, Esther and Hansen, Christian and Newey, Whitney and Robins, James},
journal = {Econom. J.},
number = {1},
pages = {C1--C68},
title = {{Double/debiased machine learning for treatment and structural parameters}},
volume = {21},
year = {2018}
}
@article{Nie2021,
author = {Nie, X and Wager, S},
journal = {Biometrika},
number = {2},
pages = {299--319},
title = {{Quasi-oracle estimation of heterogeneous treatment effects}},
volume = {108},
year = {2021}
}
@article{robinson_partially_linear,
 author = {P. M. Robinson},
 journal = {Econometrica},
 number = {4},
 pages = {931--954},
 title = {Root-N-Consistent Semiparametric Regression},
 volume = {56},
 year = {1988}
}
@article{chickering2004,
author = {Chickering, David Maxwell and Heckerman, David and Meek, Christopher},
title = {Large-Sample Learning of {B}ayesian Networks is {NP}-Hard},
year = {2004},
volume = {5},
journal = {J. Mach. Learn. Res.},
pages = {1287–1330}
}
@article{Lam2009,
author = {Lam, Clifford and Fan, Jianqing},
journal = {Ann. Statist.},
number = {6B},
pages = {4254--4278},
title = {Sparsistency and Rates of Convergence in Large Covariance Matrix Estimation},
volume = {37},
year = {2009}
}
@book{hastie2015,
address = {Boca Raton, FL},
author = {Hastie, Trevor and Tibshirani, Robert and Wainwright, Martin},
publisher = {Chapman \& Hall/CRC},
title = {Statistical learning with sparsity: The lasso and generalizations},
year = {2015}
}
@article{efron_lars,
author = {Bradley Efron and Trevor Hastie and Iain Johnstone and Robert Tibshirani},
title = {{Least angle regression}},
volume = {32},
journal = {Ann. Stat.},
number = {2},
pages = {407 -- 499},
year = {2004}
}
% Yeast
@article{Brem2005,
author = {Rachel B. Brem  and Leonid Kruglyak },
title = {The landscape of genetic complexity across 5,700 gene expression traits in yeast},
journal = {Proc. Natl. Acad. Sci.},
volume = {102},
number = {5},
pages = {1572-1577},
year = {2005}
}
@article{Brem2005b,
author = {Brem, Rachel B and Storey, John D and Whittle, Jacqueline and Kruglyak, Leonid},
journal = {Nature},
number = {7051},
pages = {701--703},
title = {{Genetic interactions between polymorphisms that affect gene expression in yeast}},
volume = {436},
year = {2005}
}
@article{storey2005,
    author = {Storey, John D AND Akey, Joshua M AND Kruglyak, Leonid},
    journal = {PLOS Biol.},
    title = {Multiple Locus Linkage Analysis of Genomewide Expression in Yeast},
    year = {2005},
    month = {07},
    volume = {3},
    pages = {e267},
    number = {8}
}
@article{Chen2007,
author = {Chen, Lin S and Emmert-Streib, Frank and Storey, John D},
journal = {Genome Biol.},
number = {10},
pages = {R219},
title = {{Harnessing naturally randomized transcription to infer regulatory relationships among genes}},
volume = {8},
year = {2007}
}
@article{Chen2019,
author = {Chen, Chen and Zhang, Dabao and Hazbun, Tony R and Zhang, Min},
journal = {Sci. Rep.},
number = {1},
pages = {1197},
title = {Inferring Gene Regulatory Networks from a Population of Yeast Segregants},
volume = {9},
year = {2019}
}
@article{perkovic2018,
  author  = {Emilija Perkovi\'c and Johannes Textor and Markus Kalisch and Marloes H. Maathuis},
  title   = {Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs},
  journal = {J. Mach. Learn. Res.},
  year    = {2018},
  volume  = {18},
  number  = {220},
  pages   = {1--62}
}
@article{robins_consistency,
 author = {James M. Robins and Richard Scheines and Peter Spirtes and Larry Wasserman},
 journal = {Biometrika},
 number = {3},
 pages = {491--515},
 title = {Uniform Consistency in Causal Inference},
 volume = {90},
 year = {2003}
}
@inproceedings{shpitser2010,
author = {Shpitser, Ilya and VanderWeele, Tyler and Robins, James M.},
title = {On the Validity of Covariate Adjustment for Estimating Causal Effects},
year = {2010},
booktitle = {Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence},
pages = {527–536}
}
@article{richardson_mags,
author = {Thomas Richardson and Peter Spirtes},
title = {{Ancestral graph Markov models}},
volume = {30},
journal = {Ann. Statist.},
number = {4},
pages = {962 -- 1030},
year = {2002}
}
