jw1212/awesome-causal-inference

Causal inference reading list

A Collection of Causal Literature

A curated reading list of papers on causal inference and causal machine learning. For other resources on causal inference, see for example Awesome Causal Inference.

Table of Contents

• Books
• Heterogeneous Treatment Effects
• Sensitivity Analysis
• Proximal Causal Inference
• Spatial Confounding
• Panel Data
• Causal Representation Learning and Invariance
• Misc

Books

• A First Course in Causal Inference - Ding (2023)

• Causal Inference: What If - Hernán, Robins (2020)

• Causal Inference in Statistics: A Primer - Pearl, Glymour, Jewell (2016)

• Causal Inference for Statistics, Social, and Biomedical Sciences - Imbens, Rubin (2015)

• Data Analysis Using Regression and Multilevel/Hierarchical Models - Gelman, Hill (2006)

Heterogeneous Treatment Effects

• Towards Optimal Doubly Robust Estimation of Heterogeneous Causal Effects - Kennedy (2023)

• Quasi-Oracle Estimation of Heterogeneous Treatment Effects - Nie and Wager (2020)

• Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion) - Hahn, Murray, Carvalho (2020)

• Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning - Künzel, Sekhon, Bickel, Yu (2019)

• Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition - Dorie et al. (2019)

• Estimation and Inference of Heterogeneous Treatment Effects using Random Forests - Wager and Athey (2018)

• Estimating individual treatment effect: generalization bounds and algorithms - Shalit, Johansson, Sontag (2017)

• Double/Debiased Machine Learning for Treatment and Structural Parameters - Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, Robins (2017)

• Bayesian Nonparametric Modeling for Causal Inference - Hill (2011)

Sensitivity Analysis

• Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approach - Jin, Ren, Candès (2022)

• Long Story Short: Omitted Variable Bias in Causal Machine Learning - Chernozhukov, Cinelli, Newey, Sharma, Syrgkanis (2022)

• Making Sense of Sensitivity: Extending Omitted Variable Bias - Cinelli and Hazlett (2020)

• Sensitivity Analysis for Inverse Probability Weighting Estimators via the Percentile Bootstrap - Zhao, Small, Bhattacharya (2019)

• Sensitivity Analysis in Observational Research: Introducing the E-Value - VanderWeele and Ding (2017)

• A Distributional Approach for Causal Inference Using Propensity Scores - Tan (2006)

• Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome - Rosenbaum and Rubin (1983)

• Smoking and Lung Cancer: Recent Evidence and a Discussion of Some Questions - Cornfield et al. (1959)

Proximal Causal Inference

• An Introduction to Proximal Causal Learning - Tchetgen Tchetgen, Ying, Cui, Shi, Miao (2020)

• A Selective Review of Negative Control Methods in Epidemiology - Shi, Miao, Tchetgen Tchetgen (2020)

• Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder - Miao, Geng, Tchetgen Tchetgen (2018)

Spatial Confounding

• A Causal Inference Framework for Spatial Confounding - Gilbert, Datta, Casey, Ogburn (2023)

• Spatial+: A novel approach to spatial confounding - Dupont, Wood, Augustin (2022)

• A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications - Reich et al. (2021)

• Mitigating Unobserved Spatial Confounding when Estimating the Effect of Supermarket Access on Cardiovascular Disease Deaths - Schnell, Papadogeorgou (2020)

Panel Data

• Estimating the effects of a California gun control program with Multitask Gaussian Processes - Ben-Michael, Arbour, Feller, Franks, Raphael (2023)

• On the Assumptions of Synthetic Control Methods - Shi, Sridhar, Misra, Blei (2022)

• Theory for identification and Inference with Synthetic Controls: A Proximal Causal Inference Framework - Shi et al. (2021)

• Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects - Abadie (2021)

• Combining Matching and Synthetic Control to Tradeoff Biases From Extrapolation and Interpolation - Kellogg et al. (2021)

• A Penalized Synthetic Control Estimator for Disaggregated Data - Abadie and L'hour (2021)

• The Augmented Synthetic Control Method - Ben-Michael, Feller, Rothstein (2021)

• Inferring causal impact using Bayesian structural time-series models - Brodersen et al. (2015), CausalImpact package

• Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program - Abadie, Diamond, Hainmueller (2010)

Causal Representation Learning and Invariance

• Desiderata for Representation Learning: A Causal Perspective - Wang and Jordan (2021)

• Toward Causal Representation Learning - Schölkopf et al. (2021)

• Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests - Veitch, D'Amour, Yadlowsky, Eisenstein (2021)

• A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms - Bengio et al. (2019)

• Causal Inference by using Invariant Prediction: Identification and Confidence Intervals - Peters, Bühlmann, Meinshausen (2016)

Misc

• Bayesian causal inference: a critical review - Li, Ding, Mealli (2022)

• A Crash Course in Good and Bad Controls - Cinelli, Forney, Pearl (2022)

• Algorithmic Fairness: Choices, Assumptions, and Definitions - Mitchell et al. (2021)

• Causal Inference using Gaussian Processes with Structured Latent Confounders - Witty et al. (2020)

• The Central Role of the Propensity Score in Observational Studies for Causal Effects - Rosenbaum and Rubin (1983)