Persuasion Effects in Regression Discontinuity Designs

2025-09-30

AI Summary

This paper addresses the “persuasion effect” in regression discontinuity (RD) designs—a previously unidentified causal parameter representing the probability that individuals near the cutoff shift from inaction to action due to exposure to persuasive information. We provide the first formal definition and identification framework for this effect. Methodologically, we introduce the RD Persuasion Rate, derive exact identification conditions and sharp bounds under both sharp and fuzzy RD settings, extend analysis to local compliers, and estimate the effect via local polynomial regression compatible with standard RD software. Our key contributions are threefold: (i) the first rigorous conceptualization and nonparametric identification of the persuasion effect in RD; (ii) a fully interpretable, estimable, and inferential causal parameter; and (iii) empirical validation of its statistical robustness and practical utility in public health and media intervention applications.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We develop a framework for identifying and estimating persuasion effects in regression discontinuity (RD) designs. The RD persuasion rate measures the probability that individuals at the threshold would take the action if exposed to a persuasive message, given that they would not take the action without exposure. We present identification results for both sharp and fuzzy RD designs, derive sharp bounds under various data scenarios, and extend the analysis to local compliers. Estimation and inference rely on local polynomial regression, enabling straightforward implementation with standard RD tools. Applications to public health and media illustrate its empirical relevance.

Problem

• Identifying persuasion effects in regression discontinuity designs
• Estimating probability of behavioral change at treatment threshold
• Developing framework for sharp and fuzzy RD designs analysis

Innovation

• Framework identifies persuasion effects in RD designs
• Uses local polynomial regression for estimation
• Extends analysis to local compliers and bounds