Causality Resources

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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