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causal-inference.org
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TitleAuthorsYearLinkTypeVenueCategory
Difficulty level
SummaryDiscussion dateCode
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Currently reading
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On negative outcome control of unobserved confounding as a generalization of difference-in-differences
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5322866/
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Near-Optimal Reinforcement Learning in Dynamic Treatment RegimesJunzhe Zhang and Elias Bareinboim2019
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Inferring causal impact using Bayesian structural time-series modelsBrodersen et al.2015
7
Off-Policy Policy Gradient with State Distribution Correction
Yao Liu, Adith Swaminathan, Alekh Agarwal, Emma Brunskill
2019
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Semiparametric Estimation of Structural Functions in Nonseparable Triangular Models
https://arxiv.org/abs/1711.02184
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Rethinking statistical learning theory: learning using statistical invariantsVapnik2019
https://link.springer.com/article/10.1007/s10994-018-5742-0
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An Introduction to the BootstrapEfron, Tibshirani1994
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Reliable Decision Support using Counterfactual ModelsPeter Schulam, Suchi Saria2019
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Introduction to RKHS, and some simple kernel algorithmsArthur Gretton2019
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Bayesian inference for causal effects: The role of randomization. TheRubin, D. B.1978
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Let's Take the Con Out of EconometricsLeamer1983
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Explanation in Causal Inference: Methods for Mediation and InteractionTyler VanderWeele2015
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Learning When-to-Treat PoliciesNie, Brunskill, Wager2019
https://arxiv.org/abs/1905.09751
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Removing Hidden Confounding by Experimental GroundingKallus et al. 2019
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Invariant Risk Minimization
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, David Lopez-Paz
2019
https://arxiv.org/abs/1907.02893
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A review of some recent advances in causal inferenceMaathuis
https://arxiv.org/pdf/1506.07669.pdf
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Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease.
Hernan et al.2008
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Causal inference for ordinal outcomes
Alexander Volfovsky, Edoardo M. Airoldi, Donald B. Rubin
2015
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Metalearners for estimating heterogeneous treatment effects using machine learning
Künzel et al.2019
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Bounds in continuous instrumental variable modelsFlorian F Gunsilius2019
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Kernel Instrumental Variable Regression
Rahul Singh, Maneesh Sahani, Arthur Gretton
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UCL Course on RLDavid Silver2015
http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
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Off-Policy Evaluation in Partially Observable EnvironmentsTenneholtz, Mannor, Shalit2019
https://arxiv.org/pdf/1909.03739.pdf
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Quasi-Oracle Estimation of Heterogeneous Treatment EffectsNie, Wager
https://arxiv.org/abs/1712.04912
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Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
Kügelgen et al.2019
http://proceedings.mlr.press/v89/kugelgen19a/kugelgen19a.pdf
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Learning Representation with Causal Invariance ICLR 2019 keynote2019
https://leon.bottou.org/talks/invariances
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6.S897/HST.956: Machine Learning for HealthcareDavid Sontag, Peter Szolovits2019
https://mlhc19mit.github.io
David Sontag, Peter Szolovits
2019
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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
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Characterization of Overlap in Observational StudiesJohannsson et al.2019
https://arxiv.org/pdf/1907.04138.pdf
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Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models
Oberst, Sontag2019
https://arxiv.org/pdf/1905.05824.pdf
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Human-level control through deep reinforcement learningMnih et al.2015
https://www.nature.com/articles/nature14236?wm=book_wap_0005
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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
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Propensity score matching with time-dependent covariatesLu B2005
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Introduction to Causal InferenceMaya L. Petersen & Laura B. Balzer2018
https://www.ucbbiostat.com
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Review
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Foundations and new horizons for causal inferenceOberwolfach
https://www.mfo.de/document/1922/preliminary_OWR_2019_25.pdf
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MPI
https://ei.is.tuebingen.mpg.de/uploads/ckeditor/attachments/909/sab2019_causal_interface.pdf
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Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics
Imbens2019
https://arxiv.org/pdf/1907.07271.pdf
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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
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Opinions
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Data science is science’s second chance to get causal inference right. A classification of data science tasks
Hernan
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Causal Discovery
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Causal discovery algorithms: A practical guideDaniel Malinsky, David Danks2017Review
52
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
54
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 progress2018
https://academic.oup.com/nsr/article/5/1/26/4638533
Review, Paper
Causal Inference
3/10
Good review paper for year 2018
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Causal Discovery: Machine Learning
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Nonlinear causal discovery with additive noise modelsHoyer et al.2009
https://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models
Paper
Causal Inference
10/10Exciting31/07/2018; 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
7/10Succinct27/06/2018
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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 Pearl2018
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
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Preface to the ACM TIST Special Issue on Causal Discovery and InferenceKun 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 EffectsSusan 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
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 BookHernan and RobinsTBABook
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 DataNathan Kallus2017Paper
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 overviewJudea Pearl2009
Technical Report
Causal Inference, Statistics
76
Causal Inference in Statistics - A PrimerJudea Pearl2016
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;
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Cause and Correlation in BiologyBill Shipley
78
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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Deconfounding Reinforcement Learning in Observational SettingsChaoChao 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 MJ2010
83
Causal Inference: Machine Learning: Causal Forests
84
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Stefan Wager & Susan Athey2017Paper
85
Generalized Random Forests
Susan Athey, Julie Tibshirani, Stefan Wager
2018Paper
86
Estimating Treatment Effects with Causal Forests: An ApplicationStefan Wager & Susan Athey2018
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. al2018
https://www.kellogg.northwestern.edu/faculty/gordon_b/files/fb_comparison.pdf
Paper
89
Biologoy
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A review of causal inference for biomedical informatics
Samantha Kleinberg, George Hripcsak
2011Paper, Review
Causal Inference, Biology, Bioinformatics
91
Causal stability rankingStekhoven et al.2012PaperBioinformatics7/10
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
2016PaperBioinformatics
93
How difficult is inference of mammalian causal gene regulatory networks?Djordjevic D et al.2014PaperBioinformatics
94
An Evaluation of Active Learning Causal Discovery Methods for Reverse-Engineering Local Causal Pathways of Gene Regulation.
Ma S et al.2016PaperBioinformatics
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
PaperStatistics4/10Controversial
97
Kernel Methods for Measuring Independence
Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, Bernhard Schölkopf
2005PaperStatistics10/10
98
Classifier Technology and the Illusion of ProgressDavid J. Hand2006PaperStatistics
99
Statistical Modeling: The Two CulturesLeo Breiman2001Paper
Statistics, Philosophy of Science
3/10
Extremely powerful
100
Intervention and Identifiability in Latent Variable ModellingJan-Willem RomeijnTBAPaperStatistics
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