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)
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]