Estimating the Complier Average Causal Effect with Non-Ignorable Missing Outcomes Using Likelihood Analysis

Jierui Du, Gao Wen and Xin Liang ()
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Jierui Du: School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
Gao Wen: School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
Xin Liang: School of Mathematics and Statistics, Guangxi Normal University, Guilin 541004, China

Mathematics, 2024, vol. 12, issue 9, 1-16

Abstract: 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 identification assumptions. The missing data mechanism is assumed to follow a logistic model, wherein the absence of the outcome is explained by the outcome itself, the treatment received, and the covariates. We establish the identifiability of the models under mild conditions by assuming that the outcome follows a normal distribution. We develop a computational method to estimate model parameters through a two-step likelihood estimation approach, employing subgroup analysis. The bootstrap method is employed for variance estimation, and the effectiveness of our approach is confirmed through simulation. We applied the proposed method to analyze the household income dataset from the Chinese Household Income Project Survey 2013.

Keywords: complier average causal effect; non-ignorable missingness; identifiability; likelihood; subgroup analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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