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awesome-causal-inference

Causal inference reading list
https://github.com/jw1212/awesome-causal-inference

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)
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 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)
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)
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
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](https://google.github.io/CausalImpact/CausalImpact.html)
Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program - Abadie, Diamond, Hainmueller (2010)
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)
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)