2026 Fall

Published

09 September 2026

Meeting Schedule

Note: We will meet weekly on Tuesdays, 2:00–4:00PM, almost always in 2-426 (except for on 22nd September).
Date Room Topic Reading Presenter
8th September 2-426 kickoff meeting NSH
15th September 2-426 foundations of targeted maximum likelihood van der Laan and Rubin (2006); and Appx. A.1–A.3 of van der Laan and Rose (2011) NB
22nd September 2-401 research connections SVB
29th September 2-426 foundations of targeted maximum likelihood Gruber and van der Laan (2010); and Appx. A.4–A.6 of van der Laan and Rose (2011) CT
6th October 2-426 canceled
13th October 2-426 double machine learning Chernozhukov et al. (2018) CT
20th October 2-426 research connections NB
27th October 2-426 orthogonal statistical learning Foster and Syrgkanis (2023) SVB
3rd November 2-426 research connections CT
10th November 2-426 doubly robust inference Benkeser et al. (2017) NB
17th November 2-426 research connections CJ
24th November 2-426 canceled—thanksgiving
1st December 2-426 research connections CT
8th December 2-426 Neyman orthogonality and pathwise differentiability Chen et al. (2026) SJB
15th December 2-426 On the Gaussian process limit of BART McCartan and Huang (2026) SVB

Our theme this term is foundations of causal machine learning, with a heavy focus on contemporary frameworks, particularly targeted learning (van der Laan and Rubin 2006; Gruber and van der Laan 2010; van der Laan and Rose 2011) to construct asymptotically efficient estimators of pathwise differentiable statistical parameters and double machine learning for Neyman-orthogonal statistical parameters (Chernozhukov et al. 2018; Foster and Syrgkanis 2023). To deepen our perspective, we will consider topics that cut across these frameworks and stretch into semi-parametric efficiency theory, including doubly robust inference (Benkeser et al. 2017), the relationship between pathwise differentiability and Neyman orthogonality (Chen et al. 2026), and recent advances in the estimation of nuisance functions (McCartan and Huang 2026). Just as with Spring 2026, meetings will alternate weekly between chalk-talk presentations of the assigned readings and informal research-connection talks intended to explore overlap between this theme and areas of ongoing research in our group.

Time permitting, we may discuss additional topics—listed below—which may replace readings in the above list (pending group discussion) or otherwise serve as potential readings for Spring 2027.

References

Bang, Heejung, and James M Robins. 2005. “Doubly Robust Estimation in Missing Data and Causal Inference Models.” Biometrics 61 (4): 962–73. https://doi.org/10.1111/j.1541-0420.2005.00377.x.
Benkeser, David, Marco Carone, Mark J van der Laan, and Peter B Gilbert. 2017. “Doubly Robust Nonparametric Inference on the Average Treatment Effect.” Biometrika 104 (4): 863–80. https://doi.org/10.1093/biomet/asx053.
Chen, Yuxi, Edward H Kennedy, and Sivaraman Balakrishnan. 2026. “On the Equivalence Between Neyman Orthogonality and Pathwise Differentiability.” arXiv Preprint arXiv:2603.15817. https://arxiv.org/abs/2603.15817.
Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, et al. 2018. “Double/Debiased Machine Learning for Treatment and Structural Parameters.” The Econometrics Journal 21 (1): C1–68. https://doi.org/10.1111/ectj.12097.
Chernozhukov, Victor, Whitney Newey, and Vasilis Syrgkanis. 2024. “Conditional Influence Functions.” arXiv Preprint arXiv:2412.18080. https://arxiv.org/abs/2412.18080.
Dı́az, Iván, Katherine L Hoffman, and Nima S Hejazi. 2024. “Causal Survival Analysis Under Competing Risks Using Longitudinal Modified Treatment Policies.” Lifetime Data Analysis 30 (1): 213–36. https://doi.org/10.1007/s10985-023-09606-7.
Dı́az, Iván, Nicholas T Williams, Paweł Morzywołek, and Kara E Rudolph. 2026. “Modified Treatment Policies That Depend on the Natural History of Treatment.” arXiv Preprint arXiv:2605.24167. https://arxiv.org/abs/2605.24167.
Dı́az, Iván, Nicholas Williams, Katherine L Hoffman, and Edward J Schenck. 2021. “Nonparametric Causal Effects Based on Longitudinal Modified Treatment Policies.” Journal of the American Statistical Association 118 (542): 846–57. https://doi.org/10.1080/01621459.2021.1955691.
Foster, Dylan J, and Vasilis Syrgkanis. 2023. “Orthogonal Statistical Learning.” The Annals of Statistics 51 (3): 879–908. https://doi.org/10.1214/23-AOS2258.
Gruber, Susan, and Mark J van der Laan. 2010. “A Targeted Maximum Likelihood Estimator of a Causal Effect on a Bounded Continuous Outcome.” The International Journal of Biostatistics 6 (1): Article 26. https://doi.org/10.2202/1557-4679.1260.
Kennedy, Edward H. 2023. “Towards Optimal Doubly Robust Estimation of Heterogeneous Causal Effects.” Electronic Journal of Statistics 17 (2): 3008–49. https://doi.org/10.1214/23-EJS2157.
Klaassen, Chris A J. 1987. “Consistent Estimation of the Influence Function of Locally Asymptotically Linear Estimators.” The Annals of Statistics 15 (4): 1548–62.
Lan, Hui, and Vasilis Syrgkanis. 2024. “Causal q-Aggregation for CATE Model Selection.” Proceedings of the 27th International Conference on Artificial Intelligence and Statistics 238: 4366–74.
Liu, Lin, Rajarshi Mukherjee, Whitney K Newey, and James M Robins. 2017. “Semiparametric Efficient Empirical Higher Order Influence Function Estimators.” arXiv Preprint arXiv:1705.07577. https://arxiv.org/abs/1705.07577.
Liu, Lin, Rajarshi Mukherjee, and James M Robins. 2020. “On Nearly Assumption-Free Tests of Nominal Confidence Interval Coverage for Causal Parameters Estimated by Machine Learning.” Statistical Science 35 (3): 518–39. https://doi.org/10.1214/20-STS786.
Luedtke, Alexander R, Oleg Sofrygin, Mark J van der Laan, and Marco Carone. 2018. “Sequential Double Robustness in Right-Censored Longitudinal Models.” arXiv Preprint arXiv:1705.02459. https://arxiv.org/abs/1705.02459.
Luedtke, Alex, and Incheoul Chung. 2024. “One-Step Estimation of Differentiable Hilbert-Valued Parameters.” The Annals of Statistics 52 (4): 1534–63. https://doi.org/10.1214/24-AOS2403.
McCartan, Cory, and Melody Huang. 2026. “Seeing the Forest for the Trees: The Gaussian Process Limit of BART.” arXiv Preprint arXiv:2607.28844. https://arxiv.org/abs/2607.28844.
Murphy, Susan A. 2003. “Optimal Dynamic Treatment Regimes.” Journal of the Royal Statistical Society Series B 65 (2): 331–55. https://doi.org/10.1111/1467-9868.00389.
Robins, James M, and Andrea Rotnitzky. 1995. “Semiparametric Efficiency in Multivariate Regression Models with Missing Data.” Journal of the American Statistical Association 90 (429): 122–29. https://doi.org/10.2307/2291135.
Robins, James M, Andrea Rotnitzky, and Lue Ping Zhao. 1994. “Estimation of Regression Coefficients When Some Regressors Are Not Always Observed.” Journal of the American Statistical Association 89 (427): 846–66. https://doi.org/10.1080/01621459.1994.10476818.
Rotnitzky, Andrea, James Robins, and Lueny Babino. 2017. “On the Multiply Robust Estimation of the Mean of the g-Functional.” arXiv Preprint arXiv:1705.08582. https://arxiv.org/abs/1705.08582.
van der Laan, Lars, Aurélien Bibaut, Nathan Kallus, and Alex Luedtke. 2025. “Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric m-Estimands.” arXiv Preprint arXiv:2501.11868. https://arxiv.org/abs/2501.11868.
van der Laan, Lars, and Mark van der Laan. 2026. “Calibeating Prediction-Powered Inference.” arXiv Preprint arXiv:2604.21260. https://arxiv.org/abs/2604.21260.
van der Laan, Mark J. 2006. “Statistical Inference for Variable Importance.” The International Journal of Biostatistics 2 (1). https://doi.org/10.2202/1557-4679.1008.
van der Laan, Mark J. 2014. “Targeted Estimation of Nuisance Parameters to Obtain Valid Statistical Inference.” The International Journal of Biostatistics 10 (1): 29–57. https://doi.org/10.1515/ijb-2012-0038.
van der Laan, Mark J, and Sherri Rose. 2011. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer. https://doi.org/10.1007/978-1-4419-9782-1.
van der Laan, Mark J, and Daniel Rubin. 2006. “Targeted Maximum Likelihood Learning.” The International Journal of Biostatistics 2 (1): 1–38. https://doi.org/10.2202/1557-4679.1043.
Westling, Ted, Peter B Gilbert, and Marco Carone. 2020. “Causal Isotonic Regression.” Journal of the Royal Statistical Society Series B 82 (3): 719–47. https://doi.org/10.1111/rssb.12372.