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1 | Welcome to the Causal Inference Reading List Visit us at causal-inference.org | ||||||||||||||||||||||||||||||||||||

2 | Title | Authors | Year | Type | Venue | Category | Difficulty level | Summary | Discussion date | Link | Code | ||||||||||||||||||||||||||

3 | Causal Discovery | ||||||||||||||||||||||||||||||||||||

4 | Causal discovery algorithms: A practical guide | Daniel Malinsky, David Danks | 2017 | Review | |||||||||||||||||||||||||||||||||

5 | Nested Markov Properties for Acyclic Directed Mixed Graphs | Thomas S. Richardson, Robin J. Evans, James M. Robins, Ilya Shpitser | 2017 | Paper | |||||||||||||||||||||||||||||||||

6 | Causal inference and the data-fusion problem | Judea Pearl and Elias Bareinboim | 2016 | Paper | Causal Inference | 5/10 | Good analysis of data sources | ||||||||||||||||||||||||||||||

7 | Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference | Dominik Janzing, Rafael Chaves, Bernhard Schoelkopf | 2016 | Paper | Causal Inference | 7/10 | SO COOL!!! | ||||||||||||||||||||||||||||||

8 | Learning causality and causality-related learning: some recent progress | 2018 | Review, Paper | Causal Inference | 3/10 | Good review paper for year 2018 | https://academic.oup.com/nsr/article/5/1/26/4638533 | ||||||||||||||||||||||||||||||

9 | Causal Discovery: Machine Learning | ||||||||||||||||||||||||||||||||||||

10 | Nonlinear causal discovery with additive noise models | Hoyer et al. | 2009 | Paper | Causal Inference | 10/10 | Exciting | 31/07/2018; 07/08/2018 | https://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models | http://webdav.tuebingen.mpg.de/causality/ | |||||||||||||||||||||||||||

11 | Elements of Causal Inference | Bernhard Schölkopf, Jonas Peters and Dominik Janzing | 2017 | Book | Causal Inference | 7/10 | Succinct | 27/06/2018 | https://www.dropbox.com/s/o4345krw428kyld/11283.pdf?dl=0 | ||||||||||||||||||||||||||||

12 | Detecting non-causal artifacts in mulitvariate linear regression models | Dominik Janzing and Bernhard Schölkopf | 2018 | Paper | Causal Inference | ||||||||||||||||||||||||||||||||

13 | A Statistician’s Re-Reaction to The Book of Why | Judea Pearl | 2018 | Blog | Causal Inference | 2/10 | http://causality.cs.ucla.edu/blog/index.php/2018/06/15/a-statisticians-re-reaction-to-the-book-of-why/ | ||||||||||||||||||||||||||||||

14 | Preface to the ACM TIST Special Issue on Causal Discovery and Inference | Kun Zhang et. al | 2016 | Paper | |||||||||||||||||||||||||||||||||

15 | Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks | Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, Bernhard Schölkopf | 2016 | Paper | Causal Inference | page 91: apparently Dominik Janzing's four year old child contributed to the data generation efforts for the CauseEffect data set; | |||||||||||||||||||||||||||||||

16 | Machine Learning Methods for Estimating Heterogeneous Causal Effects | Susan Athey and Guido W. Imbens | 2015 | Paper | |||||||||||||||||||||||||||||||||

17 | Causal Discovery: Statistical | ||||||||||||||||||||||||||||||||||||

18 | Group invariance principles for causal generative models | Michel Besserve, Naji Shajarisales, Bernhard Schölkopf, Dominik Janzing | 2017 | Paper | Causal Inference | https://arxiv.org/abs/1705.02212 | |||||||||||||||||||||||||||||||

19 | Structural causal models for macro-variables in time-series | Dominik Janzing, Paul Rubenstein, Bernhard Schölkopf | 2018 | Paper | Causal Inference | https://arxiv.org/abs/1804.03911 | |||||||||||||||||||||||||||||||

20 | Causal Inference | ||||||||||||||||||||||||||||||||||||

21 | Causal Inference Book | Hernan and Robins | TBA | Book | Causal Inference | ||||||||||||||||||||||||||||||||

22 | Optimal Balancing of Time-Dependent Confounders for Marginal Structural Models | Nathan Kallus, Michele Santacatterina | 2018 | ||||||||||||||||||||||||||||||||||

23 | Recursive Partitioning for Personalization using Observational Data | Nathan Kallus | 2017 | Paper | Causal Inference, Statistics | ||||||||||||||||||||||||||||||||

24 | The Central Role of the Propensity Score in Observational Studies for Causal Effects | Paul R. Rosenbaum; Donald B. Rubin | |||||||||||||||||||||||||||||||||||

25 | Targeted Learning: Causal Inference for Observational and Experimental Data (Springer Series in Statistics) | Mark J. van der LaanSherri Rose | |||||||||||||||||||||||||||||||||||

26 | ICML 2016 Tutorial: Causal Inference for Observational Studies | David Sontag and Uri Shalit | https://cs.nyu.edu/~shalit/tutorial.html | ||||||||||||||||||||||||||||||||||

27 | Causal Inference: Introduction | ||||||||||||||||||||||||||||||||||||

28 | Causal inference in statistics: An overview | Judea Pearl | 2009 | Technical Report | Causal Inference, Statistics | ||||||||||||||||||||||||||||||||

29 | Causal Inference in Statistics - A Primer | Judea Pearl | 2016 | Book | 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 | |||||||||||||||||||||||||||||

30 | Cause and Correlation in Biology | Bill Shipley | |||||||||||||||||||||||||||||||||||

31 | Causal Inference: Reinforcement Learning | ||||||||||||||||||||||||||||||||||||

32 | Can reinforcement learning explain the development of causal inference in multisensory integration? | Thomas H Weisswange ; Constantin A Rothkopf ; Tobias Rodemann ; Jochen Triesch | 2009 | Paper | |||||||||||||||||||||||||||||||||

33 | Causal Inference: Machine Learning | ||||||||||||||||||||||||||||||||||||

34 | Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. | Westreich D, Lessler J, Funk MJ | 2010 | ||||||||||||||||||||||||||||||||||

35 | Causal Inference: Machine Learning: Causal Forests | ||||||||||||||||||||||||||||||||||||

36 | Estimation and Inference of Heterogeneous Treatment Effects using Random Forests | Stefan Wager & Susan Athey | 2017 | Paper | |||||||||||||||||||||||||||||||||

37 | Generalized Random Forests | Susan Athey, Julie Tibshirani, Stefan Wager | 2018 | Paper | |||||||||||||||||||||||||||||||||

38 | Estimating Treatment Effects with Causal Forests: An Application | Stefan Wager & Susan Athey | 2018 | Paper | https://github.com/grf-labs/grf/blob/master/experiments/acic18/paper.pdf | ||||||||||||||||||||||||||||||||

39 | Advertisement and Marketing | ||||||||||||||||||||||||||||||||||||

40 | A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook∗ | Brett R. Gordon et. al | 2018 | Paper | https://www.kellogg.northwestern.edu/faculty/gordon_b/files/fb_comparison.pdf | ||||||||||||||||||||||||||||||||

41 | Biologoy | ||||||||||||||||||||||||||||||||||||

42 | A review of causal inference for biomedical informatics | Samantha Kleinberg, George Hripcsak | 2011 | Paper, Review | Causal Inference, Biology, Bioinformatics | ||||||||||||||||||||||||||||||||

43 | Causal stability ranking | Stekhoven et al. | 2012 | Paper | Bioinformatics | 7/10 | |||||||||||||||||||||||||||||||

44 | Methods for causal inference from gene perturbation experiments and validation | Nicolai Meinshausen, Alain Hauser, Joris M. Mooij, Jonas Peters, Philip Versteeg, and Peter Bühlmann | 2016 | Paper | Bioinformatics | ||||||||||||||||||||||||||||||||

45 | How difficult is inference of mammalian causal gene regulatory networks? | Djordjevic D et al. | 2014 | Paper | Bioinformatics | ||||||||||||||||||||||||||||||||

46 | An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation. | Ma S et al. | 2016 | Paper | Bioinformatics | ||||||||||||||||||||||||||||||||

47 | Statistics | ||||||||||||||||||||||||||||||||||||

48 | Statistics for big data: A perspective | Peter Bühlmann and Sara van de Geer | 2018 | Paper | Statistics | 4/10 | Controversial | https://www.sciencedirect.com/science/article/pii/S0167715218300610 | |||||||||||||||||||||||||||||

49 | Kernel Methods for Measuring Independence | Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, Bernhard Schölkopf | 2005 | Paper | Statistics | 10/10 | |||||||||||||||||||||||||||||||

50 | Classifier Technology and the Illusion of Progress | David J. Hand | 2006 | Paper | Statistics | ||||||||||||||||||||||||||||||||

51 | Statistical Modeling: The Two Cultures | Leo Breiman | 2001 | Paper | Statistics, Philosophy of Science | 3/10 | Extremely powerful | ||||||||||||||||||||||||||||||

52 | Intervention and Identifiability in Latent Variable Modelling | Jan-Willem Romeijn | TBA | Paper | Statistics | ||||||||||||||||||||||||||||||||

53 | To Explain or to Predict? | Galit Shmueli | 2010 | Paper | Statistics | ||||||||||||||||||||||||||||||||

54 | Nail Finder, Edifices And Oz | Leo Breiman | 1984 | Technical Report | Statistics | ||||||||||||||||||||||||||||||||

55 | A History of Parametric Statistical Inference from Bernoulli to Fisher | ||||||||||||||||||||||||||||||||||||

56 | Statistics: Independence Tests | ||||||||||||||||||||||||||||||||||||

57 | Fast Conditional Independence Test for Vector Variables with Large Sample Sizes | Krzysztof Chalupka, Pietro Perona, Frederick Eberhardt | 2018 | Paper | https://arxiv.org/abs/1804.02747 | ||||||||||||||||||||||||||||||||

58 | A Kernel Statistical Test of Independence | Gretton et al. | 2008 | Paper | Statistics | "Large-scale kernel methods for independence testing" is better | |||||||||||||||||||||||||||||||

59 | Large-scale kernel methods for independence testing | Gretton et al. | 2017 | Paper | Statistics | ||||||||||||||||||||||||||||||||

60 | Philosophy | ||||||||||||||||||||||||||||||||||||

61 | Decision-theoretic paradoxes as voting paradoxes | Rachael Briggs | 2010 | Paper | Philosophy | ||||||||||||||||||||||||||||||||

62 | Green and gure causal variables | Frederick Eberhardt | 2016 | Paper | Philosophy | ||||||||||||||||||||||||||||||||

63 | Introduction to the Epistemology of Causation | Frederick Eberhardt | 20?? | Paper | Philosophy | ||||||||||||||||||||||||||||||||

64 | How the machine ‘thinks’: Understanding opacity in machine learning algorithms | Jenna Burrell | 2016 | Paper | Philosophy | ||||||||||||||||||||||||||||||||

65 | The Anatomy of the Big Bad Bug* | Rachael Briggs | 2009 | Paper | Philosophy | ||||||||||||||||||||||||||||||||

66 | Pattern recognition between science and engineering: A red herring? | Marcello Pelilloa, Teresa Scantamburloa, Viola Schiaffonatib | 2015 | Paper | Philosophy | 5/10 | |||||||||||||||||||||||||||||||

67 | The Mythos of Model Interpretability | Zachary C. Lipton | 2016 | Paper | Philosophy | https://arxiv.org/abs/1606.03490 | |||||||||||||||||||||||||||||||

68 | The crucial role of models in science | Sabina Leonelli | 2016 | Book review | Philosophy | 3/10 | Really just a book review | ||||||||||||||||||||||||||||||

69 | Foundations of Probability | Rachael Briggs | 2015 | Paper | Philosophy, Probability | 8/10 | Very interesting perspective on the assumptions of probability theory | ||||||||||||||||||||||||||||||

70 | Interventionist counterfactuals | Rachael Briggs | 2012 | Paper | Philosophy, Causality | 8/10 | Amazing analysis of lacking langauge in Pearl's counterfactuals | ||||||||||||||||||||||||||||||

71 | Proof and Other Dilemmas : Mathematics and Philosophy | ||||||||||||||||||||||||||||||||||||

72 | Causality | ||||||||||||||||||||||||||||||||||||

73 | Causation, Prediction, and Search | Peter Spirtes, Clark Glymour and Richard Scheines | 2001 | Book | Causality | ||||||||||||||||||||||||||||||||

74 | Structural causal models for macro-variables in time-series | Janzing, Rubenstein, Schölkopf | 2018 | Paper | Causality | ||||||||||||||||||||||||||||||||

75 | Combining Experts’ Causal Judgments | Dalal Alrajeh, Hana Chockler, Joseph Yehuda Halpern | 2018 | Paper | Causality | ||||||||||||||||||||||||||||||||

76 | Axiomatizing Causal Reasoning | Joseph Y. Halpern | 2014 | Paper | Causality | ||||||||||||||||||||||||||||||||

77 | The Similarity of Causal Structures | Stephan Hartmann and Reuben Stern | Paper | Causality | http://docs.wixstatic.com/ugd/94d82d_2ff7eddcfb2143c78a619d896cefb3c9.pdf | ||||||||||||||||||||||||||||||||

78 | Robustness of Causal Claims | Judea Pearl | 2004 | Paper | Causality | http://ftp.cs.ucla.edu/pub/stat_ser/R320.pdf | |||||||||||||||||||||||||||||||

79 | Causal Explanatory Power | Paper | Causality | http://docs.wixstatic.com/ugd/94d82d_7d9cc0b0d372493ebbf9c6182b4e5545.pdf | |||||||||||||||||||||||||||||||||

80 | The Environment and Disease: Association or Causation? | Sir Austin Bradford Hill | 1965 | Paper | Causality | 3/10 | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1898525/ | ||||||||||||||||||||||||||||||

81 | The Book of Why | Judea Pearl | 2018 | Book | Causality | 3/10 | |||||||||||||||||||||||||||||||

82 | Actual Causality | Joseph Y. Halpern | 2016 | Book | Causality | 7/10 | A new approach for defining causality and such related notions as degree of responsibility, degrees of blame, and causal explanation. | ||||||||||||||||||||||||||||||

83 | Causality and Statistical Learning | Andrew Gelman | 2011 | Paper | Causality | ||||||||||||||||||||||||||||||||

84 | Causal Diagrams for Empirical Research | Judea Pearl | 1995 | Paper | Causality | ||||||||||||||||||||||||||||||||

85 | Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution | Judea Pearl | 2018 | Paper | 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 Pearl | 2018 | Paper | 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 Pearl | 2018 | Paper | Causality, Machine Learning, Causal Inference | 6/10 | |||||||||||||||||||||||||||||||

88 | 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 | 2018 | Paper | The Econometrics Journal | ? | 10/10 | ||||||||||||||||||||||||||||||

90 | Some infinity theory for Predictor Ensembles | Leo Breiman | 2000 | Paper | Machine Learning | ||||||||||||||||||||||||||||||||

91 | Heuristics of Instability and Stabilization in Model Selection | Leo Breiman | 1995 | Paper | Machine Learning | ||||||||||||||||||||||||||||||||

92 | How Mature Is the Field of Machine Learning? | Marcello Pelillo and Teresa Scantamburlo | 2013 | Paper | Machine Learing | Application of Kuhn's scientific paradigm shift onto machine learning | |||||||||||||||||||||||||||||||

93 | Ethics | ||||||||||||||||||||||||||||||||||||

94 | Why Is My Classifier Discriminatory? | Irene Chen, Fredrik D. Johansson, David Sontag | 2018 | Paper | Ethics | https://arxiv.org/abs/1805.12002 | |||||||||||||||||||||||||||||||

95 | The Machine Question | David J. Gunkel | 2012 | Book | Ethics | 5/10 | |||||||||||||||||||||||||||||||

96 | Ethics: Fairness | ||||||||||||||||||||||||||||||||||||

97 | Fair Inference On Outcomes | Razieh Nabi, Ilya Shpitser | 2017 | Paper | |||||||||||||||||||||||||||||||||

98 | Fairness of Exposure in Rankings | Ashudeep Singh, Thorsten Joachims | 2018 | Paper | |||||||||||||||||||||||||||||||||

99 | Towards Formal Definitions of Blameworthiness, Intention, and Moral Responsibility | Halpern, Kleiman-Weiner | 2018 | ||||||||||||||||||||||||||||||||||

100 | https://dsapp.uchicago.edu/projects/aequitas/ |

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