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)