2026 Lab Seminar
Paper reviews
- Multiple randomization designs: Estimation and inference with interference (Lorenzo Masoero et al., 2026)
- A nonparametric framework for treatment effect modifier discovery in high dimensions (Philippe Boileau et al., 2025)
- Alleviating the quantum Big-M problem (Edoardo Alessandroni et al., 2025)
- Balancing weights for causal inference in observational factorial studies (Ruoqi Yu & Peng Ding, 2026)
- Hilbert space embeddings and metrics on probability measures (Bharath Sriperumbudur et al., 2010)
- Kernel mean embedding of distributions: A review and beyond (Krikamol Muandet et al., 2017)
- Computational optimal transport (Gabriel Peyré & Marco Cuturi, 2019) [Ch1]
- Quantifying distributional model risk via optimal transport (Jose Blanchet & Karthyek Murthy, 2019)
- Stochastic gradient methods for distributionally robust optimization with f-divergences (Hongseok Namkoong & John Duchi, 2016)
- Augmented minimax linear estimation (David Hirshberg & Stefan Wager, 2021)
- Generalized optimal matching methods for causal inference (Nathan Kallus, 2020)
- Russian roulette: the need for stochastic potential outcomes when utilities depend on counterfactuals (Andrew Gelman & Jonas M. Mikhaeil, 2025)
- A new framework for distance and kernel-based metrics in high dimensions (Shubhadeep Chakraborty & Xianyang Zhang, 2021)
- Distinguishing genetic correlation from causation across 52 diseases and complex traits (Luke J. O’Connor & Alkes L. Price, 2018)
- Causal Inference with Generative Artificial Intelligence: Application to Texts as Treatments (Kosuke Imai & Kentaro Nakamura, 2026)
2025 Lab Seminar
Paper reviews
- Causal fairness analysis (Drago Plečko & Elias Bareinboim, 2024) [Ch1-Ch5]
- Covariate-adaptive randomization inference in matched designs (Samuel Pimentel & Yaxuan Huang, 2024)
- Sensitivity analysis for attributable effects in case2 studies (Kan Chen, Ting Ye & Dylan Small, 2025)
- Conformal inference of counterfactuals and individual treatment effects (Lihua Lei & Emmanuel Candès, 2021)
- Testing generalizability in causal inference (Daniel de Vassimon Manela, Linying Yang & Robin Evans, 2025)
- The nudge average treatment effect (Eric Tchetgen Tchetgen, 2024)
- Long story short: omitted variable bias in causal machine learning (Victor Chernozhukov et al., 2021)
- Transfer learning between U.S. presidential elections: how should we learn from a 2020 ad campaign to inform 2024 ad campaigns? (Xinran Miao, Jiwei Zhao & Hyunseung Kang, 2024)
- Causal inference on distribution functions (Zhenhua Lin, Dehan Kong & Linbo Wang, 2023)
- Game-theoretic statistics and safe anytime-valid inference (Aaditya Ramdas et al., 2023)
Book review
- High-dimensional Statistics: A Non-Asymptotic Viewpoint (Martin Wainwright, 2019)
- Hypothesis testing with e-values (Aaditya Ramdas & Ruodu Wang, 2025)
2023-24 Lab Seminar
Paper reviews
- Probabilities of causation: three counterfactual interpretations and their identification (Judea Pearl, 1999)
- Sensitivity analysis for matched observational studies with continuous exposures and binary outcomes (Jeffrey Zhang, Dylan Small & Siyu Heng, 2024)
- Robust estimation of causal effects via a high-dimensional covariate balancing propensity score (Yang Ning, Peng Sida & Kosuke Imai, 2020)
- Can we reliably detect biases that matter in observational studies? (Paul Rosenbaum, 2023)
- Semiparametric efficiency gains from parametric restrictions on propensity scores (Haruki Kono, 2024)
- Estimation based on nearest neighbor matching: from density ratio to average treatment effect (Zhexiao Lin, Peng Ding & Fang Han, 2023)
- Causal inference methods for combining randomized trials and observational studies: a review (Bénédicte Colnet et al., 2024)
- Multiple conditional randomization tests for lagged and spillover treatment effects (Yao Zhang & Qingyuan Zhao, 2024)
- Estimating causal effects under non-individualistic treatments due to network entanglement (Panos Toulis, Alexander Volfovsky & Edoardo Airoldi, 2024)
- Biased-sample empirical likelihood weighting for the missing data problems: an alternative to inverse probability weighting (Yukun Liu & Yan Fan, 2023)
- Causal inference in survival analysis under deterministic missingness of confounders in register data (Iuliana Ciocănea-Teodorescu et al., 2023)
- Evidence factors from multiple, possibly invalid, instrumental variables (Anqi Zhao et al., 2022)
- Finding influential subjects in a network using a causal framework (Youjin Lee et al., 2023)
- Neural score matching for high-dimensional causal inference (Oscar Clivio et al., 2022)
- Mendelian randomization
- Influence function and efficiency theory
Book review
- Handbook of Matching and Weighting Adjustments for Causal Inference (José Zubizarreta, Elizabeth Stuart, Dylan Small, Paul Rosenbaum, 2023)
- Causal Inference: What If (Miguel Hernán & James Robins, 2020)