Projects
Under review: Estimation of the Average Treatment Effect in Causal Survival Analysis: Practical Recommendations
This article explores causal survival analysis, which combines causal inference and survival analysis to assess the effect of a treatment on time-to-event outcomes in the presence of censoring. Specifically, we focus on estimating the ATE for time-to-event data with static treatment assignment, baseline covariates, and right-censoring.