Estimating the Complier Average Causal Effect with Non-Ignorable Missing Outcomes Using Likelihood Analysis
Estimating the Complier Average Causal Effect with Non-Ignorable Missing Outcomes Using Likelihood Analysis
Missing data problems arise in randomized trials, which complicates the inference of causal effects if the missing mechanism is non-ignorable. We tackle the challenge of identifying and estimating the complier average causal effect parameters under non-ignorable missingness by increasing the covariates to mitigate the sensitivity to the violation of specific …