21 papers · 1 filter
Nonparametric heterogeneous causal mediation with orthogonal machine learning
Jiaqi Tong, Yi Zhao, Bhramar Mukherjee +1
Causal mediation analysis decomposes the total effect of an intervention on an outcome into a direct pathway and an indirect pathway transmitted through a mediator, but standard me…
Orthogonal double residual learning for optimal individualized treatment rules
Jiaqi Tong, Fan Li
Individualized treatment rules (ITRs) map baseline characteristics to treatment recommendations, with the optimal ITR maximizing expected reward or policy welfare. Indirect methods…
Doubly robust estimation of while-alive estimands in individually-randomized and cluster-randomized trials
Xi Fang, Da Zhao, Fan Li
Randomized trials in chronic disease settings often measure treatment benefit through recurrent non-fatal events that are truncated by death, where conventional summaries either di…
Doubly cross-fit debiased machine learning of heterogeneous treatment effects under principal stratification
Jiaqi Tong, Fan Li
Principal stratification provides a foundational framework for causal inference with intermediate outcomes by defining causal effects within subpopulations, yet existing work has l…
Sensitivity analysis for causal mediation: bridge score, sharp sensitivity bounds, and calibration
Yuki Ohnishi, Fan Li
Causal mediation analysis decomposes the total treatment effect into a portion operating through a hypothesized mediator and a residual direct portion. Identification of natural di…
Sample size and power calculations for causal inference with time-to-event outcomes
Chengxin Yang, Bo Liu, Fan Li
This paper develops power and sample size formulas for causal inference with time-to-event outcomes. The target estimand is the marginal hazard ratio: the coefficient of a marginal…