Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment
arxiv(2023)
摘要
Design-based causal inference, also known as randomization-based or
finite-population causal inference, is one of the most widely used causal
inference frameworks, largely due to the merit that its statistical validity
can be guaranteed by the study design (e.g., randomized experiments) and does
not require assuming specific outcome-generating distributions or
super-population models. Despite its advantages, design-based causal inference
can still suffer from other data-related issues, among which outcome
missingness is a prevalent and significant challenge. This work systematically
studies the outcome missingness problem in design-based causal inference.
First, we propose a general and flexible outcome missingness mechanism that can
facilitate finite-population-exact randomization tests for the null effect.
Second, under this flexible missingness mechanism, we propose a general
framework called "imputation and re-imputation" for conducting
finite-population-exact randomization tests in design-based causal inference
with missing outcomes. This framework can incorporate any imputation algorithms
(from linear models to advanced machine learning-based imputation algorithms)
while ensuring finite-population-exact type-I error rate control. Third, we
extend our framework to conduct covariate adjustment in randomization tests and
construct finite-population-valid confidence sets with missing outcomes. Our
framework is evaluated via extensive simulation studies and applied to a
cluster randomized experiment called the Work, Family, and Health Study.
Open-source Python and R packages are also developed for implementation of our
framework.
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