Recovering target causal effects from post-exposure selection induced by missing outcome data
arxiv(2024)
摘要
Confounding bias and selection bias are two significant challenges to the
validity of conclusions drawn from applied causal inference. The latter can
arise through informative missingness, wherein relevant information about units
in the target population is missing, censored, or coarsened due to factors
related to the exposure, the outcome, or their consequences. We extend existing
graphical criteria to address selection bias induced by missing outcome data by
leveraging post-exposure variables. We introduce the Sequential Adjustment
Criteria (SAC), which support recovering causal effects through sequential
regressions. A refined estimator is further developed by applying Targeted
Minimum-Loss Estimation (TMLE). Under certain regularity conditions, this
estimator is multiply-robust, ensuring consistency even in scenarios where the
Inverse Probability Weighting (IPW) and the sequential regressions approaches
fall short. A simulation exercise featuring various toy scenarios compares the
relative bias and robustness of the two proposed solutions against other
estimators. As a motivating application case, we study the effects of
pharmacological treatment for Attention-Deficit/Hyperactivity Disorder (ADHD)
upon the scores obtained by diagnosed Norwegian schoolchildren in national
tests using observational data (n=9 352). Our findings support the
accumulated clinical evidence affirming a positive but small effect of
stimulant medication on school performance. A small positive selection bias was
identified, indicating that the treatment effect may be even more modest for
those exempted or abstained from the tests.
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