Causal Inference Reading List
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Welcome to the Causal Inference Reading List
Visit us at causal-inference.org
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TitleAuthorsYearTypeVenueCategory
Difficulty level
SummaryDiscussion dateLinkCode
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Causal Discovery
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Causal discovery algorithms: A practical guideDaniel Malinsky, David Danks2017Review
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Nested Markov Properties for Acyclic Directed Mixed Graphs
Thomas S. Richardson, Robin J. Evans, James M. Robins, Ilya Shpitser
2017Paper
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Causal inference and the data-fusion problemJudea Pearl and Elias Bareinboim2016Paper
Causal Inference
5/10
Good analysis of data sources
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Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference
Dominik Janzing, Rafael Chaves, Bernhard Schoelkopf
2016Paper
Causal Inference
7/10SO COOL!!!
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Learning causality and causality-related learning: some recent progress
2018Review, Paper
Causal Inference
3/10
Good review paper for year 2018
https://academic.oup.com/nsr/article/5/1/26/4638533
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Causal Discovery: Machine Learning
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Nonlinear causal discovery with additive noise modelsHoyer et al.2009Paper
Causal Inference
10/10Exciting31/07/2018; 07/08/2018
https://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models
http://webdav.tuebingen.mpg.de/causality/
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Elements of Causal Inference
Bernhard Schölkopf, Jonas Peters and Dominik Janzing
2017Book
Causal Inference
7/10Succinct27/06/2018
https://www.dropbox.com/s/o4345krw428kyld/11283.pdf?dl=0
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Detecting non-causal artifacts in mulitvariate linear regression models
Dominik Janzing and Bernhard Schölkopf
2018Paper
Causal Inference
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A Statistician’s Re-Reaction to The Book of WhyJudea Pearl2018Blog
Causal Inference
2/10
http://causality.cs.ucla.edu/blog/index.php/2018/06/15/a-statisticians-re-reaction-to-the-book-of-why/
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Preface to the ACM TIST Special Issue on Causal Discovery and Inference
Kun Zhang et. al2016Paper
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Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks
Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf
2016Paper
Causal Inference
page 91: apparently Dominik Janzing's four year old child contributed to the data generation efforts for the CauseEffect data set;
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Machine Learning Methods for Estimating Heterogeneous Causal Effects
Susan Athey and Guido W. Imbens2015Paper
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Causal Discovery: Statistical
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Group invariance principles for causal generative models
Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing
2017Paper
Causal Inference
https://arxiv.org/abs/1705.02212
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Structural causal models for macro-variables in time-series
Dominik Janzing, Paul Rubenstein, Bernhard Schölkopf
2018Paper
Causal Inference
https://arxiv.org/abs/1804.03911
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Causal Inference
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Causal Inference BookHernan and RobinsTBABook
Causal Inference
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Optimal Balancing of Time-Dependent Confounders for Marginal Structural Models
Nathan Kallus, Michele Santacatterina
2018
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Recursive Partitioning for Personalization using Observational Data
Nathan Kallus2017Paper
Causal Inference, Statistics
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The Central Role of the Propensity Score in Observational Studies for Causal Effects
Paul R. Rosenbaum; Donald B. Rubin
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Targeted Learning: Causal Inference for Observational and Experimental Data (Springer Series in Statistics)
Mark J. van der LaanSherri Rose
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ICML 2016 Tutorial: Causal Inference for Observational Studies
David Sontag and Uri Shalit
https://cs.nyu.edu/~shalit/tutorial.html
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Causal Inference: Introduction
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Causal inference in statistics: An overviewJudea Pearl2009
Technical Report
Causal Inference, Statistics
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Causal Inference in Statistics - A PrimerJudea Pearl2016Book
Causal Inference
2/10
Good explanation of Simpson's Paradox; Good explanation of model search on page 48;
https://www.amazon.co.uk/Causal-Inference-Statistics-Judea-Pearl/dp/1119186846
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Cause and Correlation in BiologyBill Shipley
31
Causal Inference: Reinforcement Learning
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Can reinforcement learning explain the development of causal inference in multisensory integration?
Thomas H Weisswange ; Constantin A Rothkopf ; Tobias Rodemann ; Jochen Triesch
2009Paper
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Causal Inference: Machine Learning
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Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.
Westreich D, Lessler J, Funk MJ2010
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Causal Inference: Machine Learning: Causal Forests
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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Stefan Wager & Susan Athey2017Paper
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Generalized Random Forests
Susan Athey, Julie Tibshirani, Stefan Wager
2018Paper
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Estimating Treatment Effects with Causal Forests: An Application
Stefan Wager & Susan Athey2018Paper
https://github.com/grf-labs/grf/blob/master/experiments/acic18/paper.pdf
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Advertisement and Marketing
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A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook∗
Brett R. Gordon et. al2018Paper
https://www.kellogg.northwestern.edu/faculty/gordon_b/files/fb_comparison.pdf
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Biologoy
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A review of causal inference for biomedical informatics
Samantha Kleinberg, George Hripcsak
2011Paper, Review
Causal Inference, Biology, Bioinformatics
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Causal stability rankingStekhoven et al.2012PaperBioinformatics7/10
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Methods for causal inference from gene perturbation experiments and validation
Nicolai Meinshausen, Alain Hauser, Joris M. Mooij, Jonas Peters, Philip Versteeg, and Peter Bühlmann
2016PaperBioinformatics
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How difficult is inference of mammalian causal gene regulatory networks?
Djordjevic D et al.2014PaperBioinformatics
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An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation.
Ma S et al.2016PaperBioinformatics
47
Statistics
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Statistics for big data: A perspective
Peter Bühlmann and Sara van de Geer
2018PaperStatistics4/10Controversial
https://www.sciencedirect.com/science/article/pii/S0167715218300610
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Kernel Methods for Measuring Independence
Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, Bernhard Schölkopf
2005PaperStatistics10/10
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Classifier Technology and the Illusion of ProgressDavid J. Hand2006PaperStatistics
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Statistical Modeling: The Two CulturesLeo Breiman2001Paper
Statistics, Philosophy of Science
3/10
Extremely powerful
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Intervention and Identifiability in Latent Variable ModellingJan-Willem RomeijnTBAPaperStatistics
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To Explain or to Predict?Galit Shmueli2010PaperStatistics
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Nail Finder, Edifices And OzLeo Breiman1984
Technical Report
Statistics
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A History of Parametric Statistical Inference from Bernoulli to Fisher
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Statistics: Independence Tests
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Fast Conditional Independence Test for Vector Variables with Large Sample Sizes
Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt
2018Paper
https://arxiv.org/abs/1804.02747
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A Kernel Statistical Test of IndependenceGretton et al.2008PaperStatistics
"Large-scale kernel methods for independence testing" is better
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Large-scale kernel methods for independence testingGretton et al.2017PaperStatistics
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Philosophy
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Decision-theoretic paradoxes as voting paradoxesRachael Briggs2010PaperPhilosophy
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Green and gure causal variablesFrederick Eberhardt2016PaperPhilosophy
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Introduction to the Epistemology of CausationFrederick Eberhardt20??PaperPhilosophy
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How the machine ‘thinks’: Understanding opacity in machine learning algorithms
Jenna Burrell2016PaperPhilosophy
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The Anatomy of the Big Bad Bug*Rachael Briggs2009PaperPhilosophy
66
Pattern recognition between science and engineering: A red herring?
Marcello Pelilloa, Teresa Scantamburloa, Viola Schiaffonatib
2015PaperPhilosophy5/10
67
The Mythos of Model InterpretabilityZachary C. Lipton2016PaperPhilosophy
https://arxiv.org/abs/1606.03490
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The crucial role of models in scienceSabina Leonelli2016Book reviewPhilosophy3/10
Really just a book review
69
Foundations of ProbabilityRachael Briggs2015Paper
Philosophy, Probability
8/10
Very interesting perspective on the assumptions of probability theory
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Interventionist counterfactualsRachael Briggs2012Paper
Philosophy, Causality
8/10
Amazing analysis of lacking langauge in Pearl's counterfactuals
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Proof and Other Dilemmas : Mathematics and Philosophy
72
Causality
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Causation, Prediction, and Search
Peter Spirtes, Clark Glymour and Richard Scheines
2001BookCausality
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Structural causal models for macro-variables in time-seriesJanzing, Rubenstein, Schölkopf2018PaperCausality
75
Combining Experts’ Causal Judgments
Dalal Alrajeh, Hana Chockler, Joseph Yehuda Halpern
2018PaperCausality
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Axiomatizing Causal ReasoningJoseph Y. Halpern2014PaperCausality
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The Similarity of Causal Structures
Stephan Hartmann and Reuben Stern
PaperCausality
http://docs.wixstatic.com/ugd/94d82d_2ff7eddcfb2143c78a619d896cefb3c9.pdf
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Robustness of Causal ClaimsJudea Pearl2004PaperCausality
http://ftp.cs.ucla.edu/pub/stat_ser/R320.pdf
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Causal Explanatory PowerPaperCausality
http://docs.wixstatic.com/ugd/94d82d_7d9cc0b0d372493ebbf9c6182b4e5545.pdf
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The Environment and Disease: Association or Causation?Sir Austin Bradford Hill1965PaperCausality3/10
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1898525/
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The Book of WhyJudea Pearl2018BookCausality3/10
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Actual CausalityJoseph Y. Halpern2016BookCausality7/10
A new approach for defining causality and such related notions as degree of responsibility, degrees of blame, and causal explanation.
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Causality and Statistical LearningAndrew Gelman2011PaperCausality
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Causal Diagrams for Empirical ResearchJudea Pearl1995PaperCausality
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Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution
Judea Pearl2018Paper
Causality, Machine Learning, Causal Inference
6/10
preprint of The Seven Tools of Causal Inference with Reflections on Machine Learning
https://arxiv.org/pdf/1801.04016.pdf
86
The Seven Pillars of Causal Reasoning with Reflections on Machine Learning
Judea Pearl2018Paper
Causality, Machine Learning, Causal Inference
6/10
preprint of The Seven Tools of Causal Inference with Reflections on Machine Learning
87
The Seven Tools of Causal Inference with Reflections on Machine Learning
Judea Pearl2018Paper
Causality, Machine Learning, Causal Inference
6/10
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Machine Learning
89
Double/Debiased Machine Learning for Treatment and Structural Parameters
Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins
2018Paper
The Econometrics Journal
?10/10
90
Some infinity theory for Predictor EnsemblesLeo Breiman2000Paper
Machine Learning
91
Heuristics of Instability and Stabilization in Model SelectionLeo Breiman1995Paper
Machine Learning
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How Mature Is the Field of Machine Learning?
Marcello Pelillo and Teresa Scantamburlo
2013Paper
Machine Learing
Application of Kuhn's scientific paradigm shift onto machine learning
93
Ethics
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Why Is My Classifier Discriminatory?
Irene Chen, Fredrik D. Johansson, David Sontag
2018PaperEthics
https://arxiv.org/abs/1805.12002
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The Machine QuestionDavid J. Gunkel2012BookEthics5/10
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Ethics: Fairness
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Fair Inference On OutcomesRazieh Nabi, Ilya Shpitser2017Paper
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Fairness of Exposure in RankingsAshudeep Singh, Thorsten Joachims2018Paper
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Towards Formal Definitions of Blameworthiness, Intention, and Moral Responsibility
Halpern, Kleiman-Weiner2018
100
https://dsapp.uchicago.edu/projects/aequitas/
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