9 papers
The Resolution of Causal Heterogeneity
Yuki Ohnishi, Fan Li
Causal subgroup analyses often report a small number of groups summarizing treatment effect heterogeneity, as if that number were a well-defined estimand. Outside genuinely latent…
Identification and estimation of causal mechanisms in cluster-randomized trials with post-treatment confounding using Bayesian nonparametrics
Yuki Ohnishi, Michael J. Daniels, Lei Yang +1
Causal mediation analysis in cluster-randomized trials (CRTs) is essential for explaining how cluster-level interventions affect individual outcomes, yet it is complicated by inter…
Principal stratification with recurrent events truncated by a terminal event: A nested Bayesian nonparametric approach
Yuki Ohnishi, Michael O. Harhay, Guangyu Tong +1
Recurrent events often serve as key endpoints in clinical studies but may be prematurely truncated by terminal events such as death, creating selection bias and complicating causal…
Forecasting Causal Effects of Future Interventions: Confounding and Transportability Issues
Laura Forastiere, Fan Li, Michela Baccini
Recent developments in causal inference allow us to transport a causal effect of a time-fixed treatment from a randomized trial to a target population across space but within the s…
Design-based inference for generalized causal effects in randomized experiments
Xinyuan Chen, Fan Li
Generalized causal effect estimands, including the Mann-Whitney parameter and causal net benefit, provide flexible summaries of treatment effects in randomized experiments with non…
A sensitivity analysis approach to principal stratification with a continuous longitudinal intermediate outcome: Applications to a cohort stepped wedge trial
Lei Yang, Michael J. Daniels, Fan Li
Causal inference in the presence of intermediate variables is a challenging problem in many applications. Principal stratification (PS) provides a framework to estimate principal c…