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1 | causal-inference.org | ||||||||||||||||||||||||||||||||||||

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

3 | Currently reading | ||||||||||||||||||||||||||||||||||||

4 | On negative outcome control of unobserved confounding as a generalization of difference-in-differences | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5322866/ | |||||||||||||||||||||||||||||||||||

5 | Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes | Junzhe Zhang and Elias Bareinboim | 2019 | ||||||||||||||||||||||||||||||||||

6 | Inferring causal impact using Bayesian structural time-series models | Brodersen et al. | 2015 | ||||||||||||||||||||||||||||||||||

7 | Off-Policy Policy Gradient with State Distribution Correction | Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill | 2019 | ||||||||||||||||||||||||||||||||||

8 | Semiparametric Estimation of Structural Functions in Nonseparable Triangular Models | https://arxiv.org/abs/1711.02184 | |||||||||||||||||||||||||||||||||||

9 | |||||||||||||||||||||||||||||||||||||

10 | Rethinking statistical learning theory: learning using statistical invariants | Vapnik | 2019 | https://link.springer.com/article/10.1007/s10994-018-5742-0 | |||||||||||||||||||||||||||||||||

11 | An Introduction to the Bootstrap | Efron, Tibshirani | 1994 | ||||||||||||||||||||||||||||||||||

12 | Reliable Decision Support using Counterfactual Models | Peter Schulam, Suchi Saria | 2019 | ||||||||||||||||||||||||||||||||||

13 | Introduction to RKHS, and some simple kernel algorithms | Arthur Gretton | 2019 | ||||||||||||||||||||||||||||||||||

14 | Bayesian inference for causal effects: The role of randomization. The | Rubin, D. B. | 1978 | ||||||||||||||||||||||||||||||||||

15 | Let's Take the Con Out of Econometrics | Leamer | 1983 | ||||||||||||||||||||||||||||||||||

16 | Explanation in Causal Inference: Methods for Mediation and Interaction | Tyler VanderWeele | 2015 | ||||||||||||||||||||||||||||||||||

17 | |||||||||||||||||||||||||||||||||||||

18 | Learning When-to-Treat Policies | Nie, Brunskill, Wager | 2019 | https://arxiv.org/abs/1905.09751 | |||||||||||||||||||||||||||||||||

19 | Removing Hidden Confounding by Experimental Grounding | Kallus et al. | 2019 | ||||||||||||||||||||||||||||||||||

20 | Invariant Risk Minimization | Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, David Lopez-Paz | 2019 | https://arxiv.org/abs/1907.02893 | |||||||||||||||||||||||||||||||||

21 | A review of some recent advances in causal inference | Maathuis | https://arxiv.org/pdf/1506.07669.pdf | ||||||||||||||||||||||||||||||||||

22 | Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. | Hernan et al. | 2008 | ||||||||||||||||||||||||||||||||||

23 | Causal inference for ordinal outcomes | Alexander Volfovsky, Edoardo M. Airoldi, Donald B. Rubin | 2015 | ||||||||||||||||||||||||||||||||||

24 | Metalearners for estimating heterogeneous treatment effects using machine learning | Künzel et al. | 2019 | ||||||||||||||||||||||||||||||||||

25 | Bounds in continuous instrumental variable models | Florian F Gunsilius | 2019 | ||||||||||||||||||||||||||||||||||

26 | Kernel Instrumental Variable Regression | Rahul Singh, Maneesh Sahani, Arthur Gretton | |||||||||||||||||||||||||||||||||||

27 | UCL Course on RL | David Silver | 2015 | http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html | |||||||||||||||||||||||||||||||||

28 | Off-Policy Evaluation in Partially Observable Environments | Tenneholtz, Mannor, Shalit | 2019 | https://arxiv.org/pdf/1909.03739.pdf | |||||||||||||||||||||||||||||||||

29 | Quasi-Oracle Estimation of Heterogeneous Treatment Effects | Nie, Wager | https://arxiv.org/abs/1712.04912 | ||||||||||||||||||||||||||||||||||

30 | Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features | Kügelgen et al. | 2019 | http://proceedings.mlr.press/v89/kugelgen19a/kugelgen19a.pdf | |||||||||||||||||||||||||||||||||

31 | Learning Representation with Causal Invariance | ICLR 2019 keynote | 2019 | https://leon.bottou.org/talks/invariances | |||||||||||||||||||||||||||||||||

32 | |||||||||||||||||||||||||||||||||||||

33 | 6.S897/HST.956: Machine Learning for Healthcare | David Sontag, Peter Szolovits | 2019 | https://mlhc19mit.github.io | David Sontag, Peter Szolovits | 2019 | |||||||||||||||||||||||||||||||

34 | A clinically applicable approach to continuous prediction of future acute kidney injury | Tomasev et al. | 2019 | https://www.nature.com/articles/s41586-019-1390-1 | |||||||||||||||||||||||||||||||||

35 | Characterization of Overlap in Observational Studies | Johannsson et al. | 2019 | https://arxiv.org/pdf/1907.04138.pdf | |||||||||||||||||||||||||||||||||

36 | Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models | Oberst, Sontag | 2019 | https://arxiv.org/pdf/1905.05824.pdf | |||||||||||||||||||||||||||||||||

37 | Human-level control through deep reinforcement learning | Mnih et al. | 2015 | https://www.nature.com/articles/nature14236?wm=book_wap_0005 | |||||||||||||||||||||||||||||||||

38 | Estimating treatment effects of longitudinal designs using regression models on propensity scores. | Achy-Brou AC1, Frangakis CE, Griswold M. | 2010 | https://www.ncbi.nlm.nih.gov/pubmed/19817741 | |||||||||||||||||||||||||||||||||

39 | Propensity score matching with time-dependent covariates | Lu B | 2005 | ||||||||||||||||||||||||||||||||||

40 | Introduction to Causal Inference | Maya L. Petersen & Laura B. Balzer | 2018 | https://www.ucbbiostat.com | |||||||||||||||||||||||||||||||||

41 | |||||||||||||||||||||||||||||||||||||

42 | Review | ||||||||||||||||||||||||||||||||||||

43 | Foundations and new horizons for causal inference | Oberwolfach | https://www.mfo.de/document/1922/preliminary_OWR_2019_25.pdf | ||||||||||||||||||||||||||||||||||

44 | MPI | https://ei.is.tuebingen.mpg.de/uploads/ckeditor/attachments/909/sab2019_causal_interface.pdf | |||||||||||||||||||||||||||||||||||

45 | Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics | Imbens | 2019 | https://arxiv.org/pdf/1907.07271.pdf | |||||||||||||||||||||||||||||||||

46 | Causal Data Science: A general framework for data fusion
and causal inference | Bareinborm | https://causalai.net/stanford-oct2019.pdf | with video: https://www.youtube.com/watch?v=wPYFuIgad_4 | |||||||||||||||||||||||||||||||||

47 | Opinions | ||||||||||||||||||||||||||||||||||||

48 | Data science is science’s second chance to get causal inference right. A classification of data science tasks | Hernan | |||||||||||||||||||||||||||||||||||

49 | |||||||||||||||||||||||||||||||||||||

50 | Causal Discovery | ||||||||||||||||||||||||||||||||||||

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

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

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

54 | 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!!! | ||||||||||||||||||||||||||||||

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

56 | Causal Discovery: Machine Learning | ||||||||||||||||||||||||||||||||||||

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

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

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

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

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

62 | 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; | |||||||||||||||||||||||||||||||

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

64 | Causal Discovery: Statistical | ||||||||||||||||||||||||||||||||||||

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

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

67 | Causal Inference | ||||||||||||||||||||||||||||||||||||

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

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

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

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

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

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

74 | Causal Inference: Introduction | ||||||||||||||||||||||||||||||||||||

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

76 | Causal Inference in Statistics - A Primer | Judea Pearl | 2016 | https://www.amazon.co.uk/Causal-Inference-Statistics-Judea-Pearl/dp/1119186846 | Book | Causal Inference | 2/10 | Good explanation of Simpson's Paradox; Good explanation of model search on page 48; | |||||||||||||||||||||||||||||

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

78 | Causal Inference: Reinforcement Learning | ||||||||||||||||||||||||||||||||||||

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

80 | Deconfounding Reinforcement Learning in Observational Settings | ChaoChao Lu | https://arxiv.org/abs/1812.10576 | ||||||||||||||||||||||||||||||||||

81 | Causal Inference: Machine Learning | ||||||||||||||||||||||||||||||||||||

82 | 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 | ||||||||||||||||||||||||||||||||||

83 | Causal Inference: Machine Learning: Causal Forests | ||||||||||||||||||||||||||||||||||||

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

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

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

87 | Advertisement and Marketing | ||||||||||||||||||||||||||||||||||||

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

89 | Biologoy | ||||||||||||||||||||||||||||||||||||

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

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

92 | 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 | ||||||||||||||||||||||||||||||||

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

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

95 | Statistics | ||||||||||||||||||||||||||||||||||||

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

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

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

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

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

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