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20172026
most citedMethodology and Real-World Applications of Dynamic Uncertain Causality Graph for Clinical Diagnosis with Explainability and Invariance

4 citations · 4 across the 10 of their papers we have counts for

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10 papers · 1 filter

stat.ME2026

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…

stat.ME2026

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…

stat.ME2026

Propensity score weighted Cox regression for survival outcomes in observational studies with multiple or factorial treatments

Zixian Zhao, Chengxin Yang, Fan Li

In observational studies with survival or time-to-event outcomes, a propensity score weighted marginal Cox proportional hazard model with the treatment variable as the only predict…

stat.ME2025

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…

stat.ME2025

On the permutation equivariance principle for causal estimands

Jiaqi Tong, Fan Li

In many causal inference problems, multiple action variables, such as factors, mediators, or network units, often share a common causal role yet lack a natural ordering. To avoid a…

stat.ME2025

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…