Multiple randomization designs: Estimation and inference with interference (Masoero et al., 2025)
A nonparametric framework for treatment effect modifier discovery in high dimensions (Boileau et al., 2025)
Alleviating the quantum Big-M problem (Alessandroni et al., 2025)
Balancing weights for causal inference in observational factorial studies (Yu and Ding, 2026)
Hilbert space embeddings and metrics on probability measures (Sriperumbudur et al., 2010)
Kernel mean embedding of distributions: A review and beyond (Muandet et al., 2017)
Computational optimal transport (Peyré and Cuturi, 2019) [Ch1]
Quantifying distributional model risk via optimal transport (Blanchet and Murthy, 2019)
Stochastic gradient methods for distributionally robust optimization with f-divergences (Namkoong and Duchi, 2016)
2025 Lab Seminar
Paper reviews
Causal fairness analysis (Plečko and Bareinboim, 2024) [Ch1-Ch5]
Covariate-adaptive randomization inference in matched designs (Pimentel and Huang, 2024)
Sensitivity analysis for attributable effects in Case2 studies (Kan et al., 2024)
Conformal inference of counterfactuals and individual treatment effects (Lei, Robins and Wasserman, 2021)
Testing generalizability in causal inference (Manela, Athey and Imbens, 2024)
The nudge average treatment effect (Tchetgen Tchetgen, 2024)
Long story short: omitted variable bias in causal machine learning (Chernozhukov, Cinelli and Newey, 2022)
Transfer learning between U.S. presidential elections: how should we learn from a 2020 ad campaign to inform 2024 ad campaigns? (Miao et al., 2024)
Causal inference on distribution functions (Lin, Kong and Wang, 2023)
Game-theoretic statistics and safe anytime-valid inference (Ramdas et al., 2023)
Book review
High-dimensional Statistics: A Non-Asymptotic Viewpoint (Martin J. Wainwright, 2019)
Hypothesis testing with e-values (Ramdas and Wang, 2025)
2023-24 Lab Seminar
Paper reviews
Probabilities of causation: three counterfactual interpretations and their identification (Pearl, 1999)
Sensitivity analysis for matched observational studies with continuous exposures and binary outcomes (Zhang, Small and Rosenbaum, 2011)
Robust estimation of causal effects via a high-dimensional covariate balancing propensity score (Ning, Peng and Imai, 2020)
Can we reliably detect biases that matter in observational studies? (Rosenbaum, 2023)
Semiparametric efficiency gains from parametric restrictions on propensity scores (Kono, 2024)
Estimation based on nearest neighbor matching: from density ratio to average treatment effect (Lin, 2023)
Causal inference methods for combining randomized trials and observational studies: a review (Colnet, Johansson and Stoye, 2024)
Multiple conditional randomization tests for lagged and spillover treatment effects (Zhang and Zhao, 2024)
Estimating causal effects under non-individualistic treatments due to network entanglement (Toulis, Kao and Airoldi, 2021)
Bias-sample empirical likelihood weighting for the missing data problem (Liu and Fan, 2023)
Causal inference in survival analysis under deterministic missingness of confounders in register data (Andersson, 2022)
Evidence factors from multiple, possibly invalid, instrumental variables (Zhao, Wang and Small, 2022)
Finding influential subjects in a network using a causal framework (Lee, Kim and Wang, 2023)
Neural score matching for high-dimensional causal inference (Clivio, Mikkelsen and van der Laan, 2022)
Mendelian randomization
Influence function and efficiency theory
Book review
Handbook of Matching and Weighting Adjustments for Causal Inference (José R. Zubizarreta, Elizabeth A. Stuart, Dylan S. Small, Paul R. Rosenbaum, 2023)
Causal Inference: What If (Miguel A. Hernán & James M. Robins, 2020)