Causal-Inference

Materials Collection for Causal Inference

Books

1. Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction 1st Edition [201mx5] [ by Guido W. Imbens (Author), Donald B. Rubin (Author)]
2. Causality: Models, Reasoning and Inference 2nd Edition [2009] [by Judea Pearl (Author)]
3. Counterfactuals and Causal Inference: Methods And Principles For Social Research (Analytical Methods for Social Research) 2nd Edition [2014] [by Stephen L. Morgan (Author)]
4. The Book of Why: The New Science of Cause and Effect [2018] [by Judea Pearl (Author), Dana Mackenzie (Author)]
5. Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series) [2017] [by Jonas Peters (Author), Dominik Janzing (Author), Bernhard Schölkopf (Author)]
6. Causation, Prediction, and Search (Lecture Notes in Statistics) [2011] [by Peter Spirtes (Author), Clark Glymour (Author), Richard Scheines (Author)]
7. Introduction to Mediation, Moderation, and Conditional Process Analysis, Second Edition: A Regression-Based Approach (Methodology in the Social Sciences) 2nd Edition [2017] [by Andrew F. Hayes (Author)]
8. Causal Inference in Statistics: A Primer 1st Edition [2016] [by Judea Pearl (Author), Madelyn Glymour (Author), Nicholas P. Jewell (Author)]
9. Advanced Data Analysis from an Elementary Point of View [forthcoming] [by Cosma Rohilla Shalizi]
10. Explanation in Causal Inference: Methods for Mediation and Interaction 1st Edition [2015] [by Tyler VanderWeele (Author)]
11. Causal Inference [forthcoming] [Hernán MA, Robins JM]

Courses

1. Causality [2017] [by Marloes Maathuis]
2. Applied Causality [2017] [by David M. Blei]
3. Applied Causality [2019] [by David M. Blei]
4. Introduction to Causal Inference for Data Science [2017] [by Mathew Kiang, Zhe Zhang, Monica Alexande]
5. Introduction to Causal Inference [2016] [by Teppei Yamamoto]
6. Counterfactual Machine Learning [2018] [by Thorsten Joachims]
7. Introduction to Causal Inference [2018] [by Maya L. Petersen & Laura B. Balzer]
8. STAT 320: Design and Analysis of Causal Studies [2014] [by Kari Lock Morgan and Fan Li]
9. Causal Inference [2015] [by Matthew Blackwell]
10. Machine Learning for Treatment Effects and Structural Equation Models [2016] [by Victor Chernozhukov]
11. Causal Diagrams: Draw Your Assumptions Before Your Conclusions [2019] [by Miguel Hernán]
12. A Crash Course in Causality: Inferring Causal Effects from Observational Data [2017] [by Jason A. Roy]

Videos and Lectures

1. Four Lectures on Causality [2017] [by Jonas Peters]
2. [Introduction to Causal Inference: Philosophy, Framework and Key Methods] [2017] [by Erica Moodie]
   1. Introduction to Causal Inference: Philosophy, Framework and Key Methods PART ONE
   2. Introduction to Causal Inference: Philosophy, Framework and Key Methods PART TWO
   3. Introduction to Causal Inference: Philosophy, Framework and Key Methods PART THREE
   4. Introduction to Causal Inference: Philosophy, Framework and Key Methods PART FOUR

Papers

Potential outcome model

1. Estimating causal effects of treatments in randomized and nonrandomized studies
2. Causal inference using potential outcomes: Design, modeling, decisions
3. Statistics and causal inference

Causal graphical model

1. Causal diagrams for empirical research

Single World Intervention Graphs (SWIGs)

1. Single world intervention graphs (SWIGs): A unification of the counterfactual and graphical approaches to causality

Discussion of potential outcome model and causal graphical model

1. Potential outcome and directed acyclic graph approaches to causality: Relevance for empirical practice in economics

Propensity score

1. The central role of the propensity score in observational studies for causal effects

Average causal effect (cross-sectional data)

1. Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions
2. Bayesian Nonparametric Modeling for Causal Inference
3. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
4. Double/Debiased Machine Learning for Treatment and Structural Parameters
5. Doubly Robust Estimation in Missing Data and Causal Inference Models
6. Matching methods for causal inference: A review and a look forward
7. An introduction to propensity score methods for reducing the effects of confounding in observational studies
8. Quasi-Oracle Estimation of Heterogeneous Treatment Effects
9. Targeted Maximum Likelihood Learning
10. Metalearners for estimating heterogeneous treatment effects using machine learning

Difference in difference

1. Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania

Instrumental variable

1. Another look at the instrumental variable estimation of error-components models
2. Identification of Causal Effects Using Instrumental Variables
3. Identification and estimation of local average treatment effects

Regression discontinuity

1. Regression discontinuity designs: A guide to practice
2. Regression discontinuity designs in economics

Synthetic control

1. Comparative politics and the synthetic control method
2. Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program

Adaptive experimentation

1. Confidence intervals for policy evaluation in adaptive experiments

Tutorials

1. Causal Inference Tutorial – ICML 2016
2. Tutorial on Causal Inference and Counterfactual Reasoning - KDD 2018
3. Graphical Models for Causal Inference - UAI 2012
4. Computational Advertising & Causality - UAI 2013
5. Non-parametric Causal Models - UAI 2015
6. Machine Learning and Counterfactual Reasoning for "Personalized" Decision-Making in Healthcare - UAI 2017
7. Causes and Counterfactuals: Concepts, Principles and Tools - NeurIPS 2013
8. Non-Parametric Causal Models - NeurIPS 2014
9. Counterfactual Inference - NeurIPS 2018
10. Causal inference at the intersection of machine learning and statistics: opportunities and challenges - AISTATS 2018
11. CAUSAL INFERENCE IN STATISTICS: A Gentle Introduction - Joint Statistical Meetings 2016

Researchers

1. Donald B Rubin
2. James M. Robins
3. Judea Pearl

WebSites

1. Judea Pearl homepage
2. Donald B. Rubin homepage
3. James Robins homepage

Datasets

1. [IHDP] R Simulation
2. [News] A dataset for treatment effect estimation as used in J, Shalit, Sontag, ICML, 2016.

Software

1. BART: Bayesian Additive Regression Trees
2. tmle: Targeted Maximum Likelihood Estimation [depend on] SuperLearner: Super Learner Prediction
3. causalToolbox provides functions for estimating heterogenous treatment effects (metalearners)
4. grf: Generalized Random Forests (Beta)
5. MatchIt: Nonparametric Preprocessing for Parametric Causal Inference (matching)
6. Matching: Multivariate and Propensity Score Matching with Balance Optimization (matching)
7. cem: Coarsened Exact Matching (cem)
8. optmatch: Functions for Optimal Matching
9. twang: Toolkit for Weighting and Analysis of Nonequivalent Groups

Contributing

Have anything in mind that you think would fit in this list? Feel free to send a pull request.