Reference

Ahrens, Achim, Christian B Hansen, Mark E Schaffer, and Thomas Wiemann. 2025. “Model Averaging and Double Machine Learning.” Journal of Applied Econometrics.
Bajari, Patrick, Zhihao Cen, Victor Chernozhukov, Manoj Manukonda, Suhas Vijaykumar, Jin Wang, Ramon Huerta, et al. 2023. “Hedonic Prices and Quality Adjusted Price Indices Powered by AI.” arXiv Preprint arXiv:2305.00044.
Behaghel, Luc, Bruno Crépon, and Marc Gurgand. 2014. “Private and Public Provision of Counseling to Job Seekers: Evidence from a Large Controlled Experiment.” American Economic Journal: Applied Economics 6 (4): 142–74.
Ben-Michael, Eli, Avi Feller, David A Hirshberg, and José R Zubizarreta. 2021. “The Balancing Act in Causal Inference.” arXiv Preprint arXiv:2110.14831.
Bruns-Smith, David, Oliver Dukes, Avi Feller, and Elizabeth L Ogburn. 2023. “Augmented Balancing Weights as Linear Regression.” arXiv Preprint arXiv:2304.14545.
Chattopadhyay, Ambarish, Christopher H Hase, and José R Zubizarreta. 2020. “Balancing Vs Modeling Approaches to Weighting in Practice.” Statistics in Medicine 39 (24): 3227–54.
Chattopadhyay, Ambarish, and José R Zubizarreta. 2023. “On the Implied Weights of Linear Regression for Causal Inference.” Biometrika 110 (3): 615–29.
Chattopadhyay, Ambarish, and José R. Zubizarreta. 2024. Harvard Data Science Review 6 (1).
Chen, Qizhao, Vasilis Syrgkanis, and Morgane Austern. 2022. “Debiased Machine Learning Without Sample-Splitting for Stable Estimators.” Advances in Neural Information Processing Systems 35: 3096–3109.
Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. 2018. “Double/Debiased Machine Learning for Treatment and Structural Parameters.” The Econometrics Journal 21 (1): C1–68.
Chernozhukov, Victor, Juan Carlos Escanciano, Hidehiko Ichimura, Whitney K Newey, and James M Robins. 2022. “Locally Robust Semiparametric Estimation.” Econometrica 90 (4): 1501–35.
Chernozhukov, Victor, Christian Hansen, Nathan Kallus, Martin Spindler, and Vasilis Syrgkanis. 2024. “Applied Causal Inference Powered by ML and AI.” arXiv Preprint arXiv:2403.02467.
Chernozhukov, Victor, Whitney K Newey, and Rahul Singh. 2022a. “Automatic Debiased Machine Learning of Causal and Structural Effects.” Econometrica 90 (3): 967–1027.
———. 2022b. “Debiased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers.” The Econometrics Journal 25 (3): 576–601.
Chernozhukov, Victor, Whitney Newey, Vıctor M Quintas-Martınez, and Vasilis Syrgkanis. 2022. “Riesznet and Forestriesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests.” In International Conference on Machine Learning, 3901–14. PMLR.
Dı́az, Iván. 2020. “Machine Learning in the Estimation of Causal Effects: Targeted Minimum Loss-Based Estimation and Double/Debiased Machine Learning.” Biostatistics 21 (2): 353–58.
Egami, Hiroyuki, Md Shafiur Rahman, Tsuyoshi Yamamoto, Chihiro Egami, and Takahisa Wakabayashi. 2024. “Causal Effect of Video Gaming on Mental Well-Being in Japan 2020–2022.” Nature Human Behaviour 8 (10): 1943–56.
Fisher, Aaron, and Edward H Kennedy. 2021. “Visually Communicating and Teaching Intuition for Influence Functions.” The American Statistician 75 (2): 162–72.
Gelman, Andrew, Jessica Hullman, and Lauren Kennedy. 2024. “Causal Quartets: Different Ways to Attain the Same Average Treatment Effect.” The American Statistician 78 (3): 267–72.
Hainmueller, Jens. 2012. “Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies.” Political Analysis 20 (1): 25–46.
Hines, Oliver, Oliver Dukes, Karla Diaz-Ordaz, and Stijn Vansteelandt. 2022. “Demystifying Statistical Learning Based on Efficient Influence Functions.” The American Statistician 76 (3): 292–304.
Huling, Jared D, and Simon Mak. 2024. “Energy Balancing of Covariate Distributions.” Journal of Causal Inference 12 (1): 20220029.
Ichimura, Hidehiko, and Whitney K Newey. 2022. “The Influence Function of Semiparametric Estimators.” Quantitative Economics 13 (1): 29–61.
Imai, Kosuke, and Marc Ratkovic. 2014. “Covariate Balancing Propensity Score.” Journal of the Royal Statistical Society Series B: Statistical Methodology 76 (1): 243–63.
Jackson, John W, and Tyler J VanderWeele. 2018. “Decomposition Analysis to Identify Intervention Targets for Reducing Disparities.” Epidemiology (Cambridge, Mass.) 29 (6): 825.
James, Gareth, Daniela Witten, Trevor Hastie, Robert Tibshirani, et al. 2021. An Introduction to Statistical Learning. Vol. 112. Springer.
Kallus, Nathan. 2023. “Treatment Effect Risk: Bounds and Inference.” Management Science 69 (8): 4579–90.
Kennedy, Edward H. 2024. “Semiparametric Doubly Robust Targeted Double Machine Learning: A Review.” Handbook of Statistical Methods for Precision Medicine, 207–36.
Ludwig, Jens, Sendhil Mullainathan, and Jann Spiess. 2019. “Augmenting Pre-Analysis Plans with Machine Learning.” In Aea Papers and Proceedings, 109:71–76. American Economic Association 2014 Broadway, Suite 305, Nashville, TN 37203.
Mossé, Milan, Kara Schechtman, Frederick Eberhardt, and Thomas Icard. 2025. “Modeling Discrimination with Causal Abstraction.” arXiv Preprint arXiv:2501.08429.
Naimi, Ashley I, Alan E Mishler, and Edward H Kennedy. 2023. “Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms.” American Journal of Epidemiology 192 (9): 1536–44.
Naimi, Ashley I, Ya-Hui Yu, and Lisa M Bodnar. 2024. “Pseudo-Random Number Generator Influences on Average Treatment Effect Estimates Obtained with Machine Learning.” Epidemiology 35 (6): 779–86.
Opacic, Aleksei, Lai Wei, and Xiang Zhou. 2025. “Disparity Analysis: A Tale of Two Approaches.” Journal of the Royal Statistical Society Series A: Statistics in Society, qnaf008.
Renson, Audrey, Lina Montoya, Dana E Goin, Iván Dı́az, and Rachael K Ross. 2025. “Pulling Back the Curtain: The Road from Statistical Estimand to Machine-Learning Based Estimator for Epidemiologists (No Wizard Required).” arXiv Preprint arXiv:2502.05363.
Rose, Evan K. 2023. “A Constructivist Perspective on Empirical Discrimination Research.” Journal of Economic Literature 61 (3): 906–23.
Schader, Lindsey, Weishan Song, Russell Kempker, and David Benkeser. 2024. “Don’t Let Your Analysis Go to Seed: On the Impact of Random Seed on Machine Learning-Based Causal Inference.” Epidemiology 35 (6): 764–78.
Schuler, Alejandro, and Mark van der Laan. 2024. “Introduction to Modern Causal Inference.” preparation.
Stuart, Elizabeth A, Gary King, Kosuke Imai, and Daniel Ho. 2011. “MatchIt: Nonparametric Preprocessing for Parametric Causal Inference.” Journal of Statistical Software.
Urminsky, Oleg, Christian Hansen, and Victor Chernozhukov. 2019. “The Double-Lasso Method for Principled Variable Selection.”
Vafa, Keyon, Susan Athey, and David M Blei. 2024. “Estimating Wage Disparities Using Foundation Models.” arXiv Preprint arXiv:2409.09894.
Varian, Hal R. 2014. “Big Data: New Tricks for Econometrics.” Journal of Economic Perspectives 28 (2): 3–28.
Wager, Stefan. 2024. “Causal Inference: A Statistical Learning Approach.” preparation.
Zhou, Xiang, and Aleksei Opacic. 2022. “Marginal Interventional Effects.” arXiv Preprint arXiv:2206.10717.
Zivich, Paul N. 2024. “Commentary: The Seedy Side of Causal Effect Estimation with Machine Learning.” Epidemiology 35 (6): 787–90.
Zubizarreta, José R. 2015. “Stable Weights That Balance Covariates for Estimation with Incomplete Outcome Data.” Journal of the American Statistical Association 110 (511): 910–22.