Treatment effects at the margin: Everyone is marginal

2025-08-29

AI Summary

This paper addresses the challenge of identifying causal effects when a policy simultaneously affects both participation in decision-making and outcome variables (e.g., employment subsidies influencing both hiring decisions and wages). We propose a marginal weighting estimator that integrates the Heckman selection model with the local average treatment effect (LATE) framework. Our method identifies causal effects for marginally induced units—such as marginal firms—under endogenous participation, overcoming key limitations of conventional approaches: standard Heckman models, LATE, difference-in-differences (DiD), and regression discontinuity design (RDD) all fail to disentangle simultaneous shifts along both the participation and outcome margins. The resulting structural causal inference framework is both interpretable and policy-relevant. We apply it to evaluate real-world institutional reforms, substantially improving the accuracy and applicability of policy effect estimation in labor, development, and health economics—particularly in settings plagued by selection bias.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

This paper develops a framework for identifying treatment effects when a policy simultaneously alters both the incentive to participate and the outcome of interest -- such as hiring decisions and wages in response to employment subsidies; or working decisions and wages in response to job trainings. This framework was inspired by my PhD project on a Belgian reform that subsidised first-time hiring, inducing entry by marginal firms yet meanwhile changing the wages they pay. Standard methods addressing selection-into-treatment concepts (like Heckman selection equations and local average treatment effects), or before-after comparisons (including simple DiD or RDD), cannot isolate effects at this shifting margin where treatment defines who is observed. I introduce marginality-weighted estimands that recover causal effects among policy-induced entrants, offering a policy-relevant alternative in settings with endogenous selection. This method can thus be applied widely to understanding the economic impacts of public programmes, especially in fields largely relying on reduced-form causal inference estimation (e.g. labour economics, development economics, health economics).

Problem

• Identifies treatment effects when policies alter participation incentives and outcomes simultaneously
• Addresses limitations of standard methods in isolating effects at shifting margins
• Provides causal effects for policy-induced entrants with endogenous selection

Innovation

• Marginality-weighted estimands for causal effects
• Framework for policy-induced entrants analysis
• Addresses endogenous selection in program evaluation