Estimating controlled direct effects with panel data: an application to reducing support for discriminatory policies

Abstract

Recent experimental studies in the social sciences have demonstrated that perspective-taking conversations are effective at reducing prejudicial attitudes and support for discriminatory policies. We ask if such interventions can directly affect policy views without changing prejudice. Unfortunately, the identification of the controlled direct effect—the natural causal quantity of interest for this question—has required strong selection-on-observables assumptions for any mediator. We leverage a recent experimental study with multiple survey waves of follow-up to identify and estimate the controlled direct effect using the changes in the outcome and mediator over time assuming parallel trends in the potential outcomes. This design allows us to weaken the identification assumptions to allow for linear and time-constant unmeasured confounding between the mediator and the outcome. We develop a semiparametrically efficient and doubly robust estimator for these quantities along with a sensitivity analysis for the key identifying assumption of parallel trends. Contrary to what traditional methods find, our approach estimates a controlled direct effect of perspective-taking conversations when subjective feelings are neutral but not positive or negative, and this result is robust to moderate departures from parallel trends.