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https://github.com/jw1212/awesome-causal-inference

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

List: awesome-causal-inference

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Causal inference reading list

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README 

        
# 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](https://github.com/matteocourthoud/awesome-causal-inference).



## Table of Contents



- [Books](#books)

- [Heterogeneous Treatment Effects](#heterogeneous-treatment-effects)

- [Sensitivity Analysis](#sensitivity-analysis)

- [Proximal Causal Inference](#proximal-causal-inference)

- [Spatial Confounding](#spatial-confounding)

- [Panel Data](#panel-data)

- [Causal Representation Learning and Invariance](#causal-representation-learning-and-invariance)

- [Misc](#misc)



## Books



- [A First Course in Causal Inference](https://arxiv.org/pdf/2305.18793.pdf) - Ding (2023)



- [Causal Inference: What If](https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/) - Hernán, Robins (2020)



- [Causal Inference in Statistics: A Primer](https://www.wiley.com/en-us/Causal+Inference+in+Statistics%3A+A+Primer-p-9781119186847) - Pearl, Glymour, Jewell (2016)



- [Causal Inference for Statistics, Social, and Biomedical Sciences](https://www.cambridge.org/core/books/causal-inference-for-statistics-social-and-biomedical-sciences/71126BE90C58F1A431FE9B2DD07938AB) - Imbens, Rubin (2015)



- [Data Analysis Using Regression and Multilevel/Hierarchical Models](https://www.cambridge.org/highereducation/books/data-analysis-using-regression-and-multilevel-hierarchical-models/32A29531C7FD730C3A68951A17C9D983#overview) - Gelman, Hill (2006)



## Heterogeneous Treatment Effects



- [Towards Optimal Doubly Robust Estimation of Heterogeneous Causal Effects](https://arxiv.org/pdf/2004.14497.pdf) - Kennedy (2023)

  

- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://arxiv.org/pdf/1712.04912.pdf) - Nie and Wager (2020)

  

- [Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion)](https://projecteuclid.org/journals/bayesian-analysis/volume-15/issue-3/Bayesian-Regression-Tree-Models-for-Causal-Inference--Regularization-Confounding/10.1214/19-BA1195.full) - Hahn, Murray, Carvalho (2020)

  

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://arxiv.org/pdf/1706.03461.pdf) - Künzel, Sekhon, Bickel, Yu (2019)

  

- [Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition](https://projecteuclid.org/journals/statistical-science/volume-34/issue-1/Automated-versus-Do-It-Yourself-Methods-for-Causal-Inference/10.1214/18-STS667.full) - Dorie et al. (2019)

  

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://www.tandfonline.com/doi/full/10.1080/01621459.2017.1319839) - Wager and Athey (2018)

  

- [Estimating individual treatment effect: generalization bounds and algorithms](https://proceedings.mlr.press/v70/shalit17a.html) - Shalit, Johansson, Sontag (2017)

  

- [Double/Debiased Machine Learning for Treatment and Structural Parameters](https://arxiv.org/pdf/1608.00060.pdf) - Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, Robins (2017)

  

- [Bayesian Nonparametric Modeling for Causal Inference](https://www.tandfonline.com/doi/abs/10.1198/jcgs.2010.08162) - Hill (2011)



## Sensitivity Analysis

  

- [Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approach](https://arxiv.org/pdf/2111.12161.pdf) - Jin, Ren, Candès (2022)



- [Long Story Short: Omitted Variable Bias in Causal Machine Learning](https://www.nber.org/system/files/working_papers/w30302/w30302.pdf) - Chernozhukov, Cinelli, Newey, Sharma, Syrgkanis (2022)



- [Making Sense of Sensitivity: Extending Omitted Variable Bias](https://academic.oup.com/jrsssb/article/82/1/39/7056023) - Cinelli and Hazlett (2020)



- [Sensitivity Analysis for Inverse Probability Weighting Estimators via the Percentile Bootstrap](https://academic.oup.com/jrsssb/article/81/4/735/7048357) - Zhao, Small, Bhattacharya (2019)



- [Sensitivity Analysis in Observational Research: Introducing the E-Value](https://www.acpjournals.org/doi/full/10.7326/M16-2607) - VanderWeele and Ding (2017)



- [A Distributional Approach for Causal Inference Using Propensity Scores](https://www.tandfonline.com/doi/abs/10.1198/016214506000000023) - Tan (2006)



- [Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome](https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.2517-6161.1983.tb01242.x) - Rosenbaum and Rubin (1983)



- [Smoking and Lung Cancer: Recent Evidence and a Discussion of Some Questions](https://academic.oup.com/jnci/article/22/1/173/912572) - Cornfield et al. (1959)



## Proximal Causal Inference



- [An Introduction to Proximal Causal Learning](https://arxiv.org/pdf/2009.10982.pdf) - Tchetgen Tchetgen, Ying, Cui, Shi, Miao (2020)

  

- [A Selective Review of Negative Control Methods in Epidemiology](https://link.springer.com/article/10.1007/s40471-020-00243-4) - Shi, Miao, Tchetgen Tchetgen (2020)



- [Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder](https://arxiv.org/pdf/1609.08816.pdf) - Miao, Geng, Tchetgen Tchetgen (2018)



## Spatial Confounding



- [A Causal Inference Framework for Spatial Confounding](https://arxiv.org/pdf/2112.14946.pdf) - Gilbert, Datta, Casey, Ogburn (2023)



- [Spatial+: A novel approach to spatial confounding](https://onlinelibrary.wiley.com/doi/full/10.1111/biom.13656) - Dupont, Wood, Augustin (2022)



- [A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications](https://onlinelibrary.wiley.com/doi/full/10.1111/insr.12452) - Reich et al. (2021)



- [Mitigating Unobserved Spatial Confounding when Estimating the Effect of Supermarket Access on Cardiovascular Disease Deaths](https://arxiv.org/pdf/1907.12150.pdf) - Schnell, Papadogeorgou

 (2020)



## Panel Data



- [Estimating the effects of a California gun control program with Multitask Gaussian Processes](https://arxiv.org/pdf/2110.07006.pdf) - Ben-Michael, Arbour, Feller, Franks, Raphael (2023)



- [On the Assumptions of Synthetic Control Methods](https://proceedings.mlr.press/v151/shi22b.html) - Shi, Sridhar, Misra, Blei (2022)



- [Theory for identification and Inference with Synthetic Controls: A Proximal Causal Inference Framework](https://arxiv.org/pdf/2108.13935) - Shi et al. (2021)



- [Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects](https://pubs.aeaweb.org/doi/pdfplus/10.1257/jel.20191450) - Abadie (2021)



- [Combining Matching and Synthetic Control to Tradeoff Biases From Extrapolation and Interpolation](https://www.tandfonline.com/doi/full/10.1080/01621459.2021.1979562) - Kellogg et al. (2021)



- [A Penalized Synthetic Control Estimator for Disaggregated Data](https://www.tandfonline.com/doi/full/10.1080/01621459.2021.1971535) -  Abadie and L'hour (2021)



- [The Augmented Synthetic Control Method](https://www.tandfonline.com/doi/full/10.1080/01621459.2021.1929245?scroll=top&needAccess=true) - Ben-Michael, Feller, Rothstein (2021)



- [Inferring causal impact using Bayesian structural time-series models](https://arxiv.org/pdf/1506.00356.pdf) - 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](https://www.tandfonline.com/doi/abs/10.1198/jasa.2009.ap08746) - Abadie, Diamond, Hainmueller (2010)



## Causal Representation Learning and Invariance



- [Desiderata for Representation Learning: A Causal Perspective](https://arxiv.org/pdf/2109.03795.pdf) - Wang and Jordan (2021)



- [Toward Causal Representation Learning](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9363924) - Schölkopf et al. (2021)



- [Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests](https://arxiv.org/pdf/2106.00545.pdf) - Veitch, D'Amour, Yadlowsky, Eisenstein (2021)



- [A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms](https://arxiv.org/pdf/1901.10912.pdf) -  Bengio et al. (2019)



- [Causal Inference by using Invariant Prediction: Identification and Confidence Intervals](https://academic.oup.com/jrsssb/article/78/5/947/7040653) - Peters, Bühlmann, Meinshausen (2016)



## Misc

 

- [Bayesian causal inference: a critical review](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2022.0153) - Li, Ding, Mealli (2022)



- [A Crash Course in Good and Bad Controls](https://journals.sagepub.com/doi/full/10.1177/00491241221099552) - Cinelli, Forney, Pearl (2022)

  

- [Algorithmic Fairness: Choices, Assumptions, and Definitions](https://www.annualreviews.org/doi/pdf/10.1146/annurev-statistics-042720-125902) - Mitchell et al. (2021)



- [Causal Inference using Gaussian Processes with Structured Latent Confounders](https://proceedings.mlr.press/v119/witty20a.html) - Witty et al. (2020)

   

- [The Central Role of the Propensity Score in Observational Studies for Causal Effects](https://academic.oup.com/biomet/article/70/1/41/240879) - Rosenbaum and Rubin (1983)