awesome-causal-inference
github.com/jw1212/awesome-causal-inference ↗Causal inference reading list
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Spatial Confounding and Spatiotemporal Causal Inference
- A Causal Inference Framework for Spatial Confounding
Gilbert et al. (2023)
- A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications
Reich et al. (2021)
- A Systematic Review of Spatio-Temporal Statistical Models: Theory, Structure, and Applications
Habereder et al. (2025)
- GST-UNet: Spatiotemporal Causal Inference with Time-Varying Confounders
Oprescu et al. (2025)
- Mitigating Unobserved Spatial Confounding when Estimating the Effect of Supermarket Access on Cardiovascular Disease Deaths
Schnell, Papadogeorgou (2020)
- Robust Spatial Confounding Adjustment via Basis Voting
Burman et al. (2025)
Misc
- A Crash Course in Good and Bad Controls
Cinelli, Forney, Pearl (2022)
- Algorithmic Fairness: Choices, Assumptions, and Definitions
Mitchell et al. (2021)
- Bayesian causal inference: a critical review
Li, Ding, Mealli (2022)
- Beyond reweighting: On the predictive role of covariate shift in effect generalization
Jin, Egami, Rothenhäusler (2025, PNAS)
- Prediction-Powered Causal Inferences
Cadei et al. (2025, NeurIPS)
- The Central Role of the Propensity Score in Observational Studies for Causal Effects
Rosenbaum and Rubin (1983)
Sensitivity Analysis
- A Distributional Approach for Causal Inference Using Propensity Scores
Tan (2006, JASA)
- Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome
Rosenbaum and Rubin (1983, JRSS-B)
- Long Story Short: Omitted Variable Bias in Causal Machine Learning
Chernozhukov, Cinelli, Newey, Sharma, Syrgkanis (2024)
- Making Sense of Sensitivity: Extending Omitted Variable Bias
Cinelli and Hazlett (2020, JRSS-B)
- Sensitivity Analysis for Inverse Probability Weighting Estimators via the Percentile Bootstrap
Zhao, Small, Bhattacharya (2019, JRSS-B)
- Sensitivity Analysis in Observational Research: Introducing the E-Value
VanderWeele and Ding (2017, Ann Intern Med)
Books
- A First Course in Causal Inference
Ding (2023)
- Applied Causal Inference Powered by ML and AI
Chernozhukov, Hansen, Kallus, Spindler, Syrgkanis (2024)
- Causal Inference for Statistics, Social, and Biomedical Sciences
Imbens, Rubin (2015)
- Causal Inference in Statistics: A Primer
Pearl, Glymour, Jewell (2016)
- Causal Inference: What If
Hernán, Robins (2020)
- Data Analysis Using Regression and Multilevel/Hierarchical Models
Gelman, Hill (2006)
Causal Representation Learning and Invariance
- 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)
- Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests
Veitch, D'Amour, Yadlowsky, Eisenstein (2021)
- Desiderata for Representation Learning: A Causal Perspective
Wang and Jordan (2021)
- Invariant Risk Minimization
Arjovsky et al. (2019)
- Toward Causal Representation Learning
Schölkopf et al. (2021)
Proximal and Multivariate 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, Curr. Epidemiol. Rep.)
- Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder
Miao, Geng, Tchetgen Tchetgen (2018, Biometrika)
- On Multi-Cause Causal Inference with Unobserved Confounding:
Counterexamples, Impossibility, and Alternatives
D’Amour (2019, AISTATS)
- Semiparametric Proximal Causal Inference
Cui et al. (2024, JASA)
- The Blessings of Multiple Causes
Wang and Blei (2019, JASA)
Panel Data
- A Penalized Synthetic Control Estimator for Disaggregated Data
Abadie and L'hour (2021)
- Estimating the effects of a California gun control program with Multitask Gaussian Processes
Ben-Michael, Arbour, Feller, Franks, Raphael (2023)
- Inferring causal impact using Bayesian structural time-series models
Brodersen et al. (2015),
- On the Assumptions of Synthetic Control Methods
Shi, Sridhar, Misra, Blei (2022)
- Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program
Abadie, Diamond, Hainmueller (2010)
- The Augmented Synthetic Control Method
Ben-Michael, Feller, Rothstein (2021)
Heterogeneous Treatment Effects
- Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition
Dorie et al. (2019)
- Bayesian Nonparametric Modeling for Causal Inference
Hill (2011)
- Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion)
Hahn, Murray, Carvalho (2020)
- Double/Debiased Machine Learning for Treatment and Structural Parameters
Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, Robins (2017)
- Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments
Colangelo and Lee (2025, JBES)
- Estimating individual treatment effect: generalization bounds and algorithms
Shalit, Johansson, Sontag (2017)
Showing a sample of 58 resources. View the full list on GitHub →