Learning the Effect of Persuasion via Difference-In-Differences

TL;DR

This paper introduces a difference-in-differences framework to accurately measure the persuasive impact of informational treatments on behavior, refining causal inference with new parameters and estimators applicable to various settings.

Contribution

It develops forward and backward average persuasion rates, providing more precise causal measures, and proposes regression-based and semiparametric estimators for diverse treatment scenarios.

Findings

Framework applied to British election data

Framework applied to Chinese curriculum reform

New estimators improve causal measurement accuracy

Abstract

We develop a difference-in-differences framework to measure the persuasive impact of informational treatments on behavior. We introduce two causal parameters, the forward and backward average persuasion rates on the treated, which refine the average treatment effect on the treated. The forward rate excludes cases of "preaching to the converted," while the backward rate omits "talking to a brick wall" cases. We propose both regression-based and semiparametrically efficient estimators. The framework applies to both two-period and staggered treatment settings, including event studies, and we demonstrate its usefulness with applications to a British election and a Chinese curriculum reform.