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)
Book review
High-dimensional Statistics: A Non-Asymptotic Viewpoint (Martin J. Wainwright, 2019)
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) [Ch11-Ch19, Ch26]
Causal Inference: What If (Miguel A. Hernán & James M. Robins, 2020) [Ch10-Ch23]