Extracting Mechanisms from Heterogeneous Effects: An Identification Strategy for Mediation Analysis

2024-03-07

AI Summary

Existing causal mediation analysis relies heavily on untestable multiple ignorability assumptions, undermining the robustness of mechanistic inference. This paper proposes a novel identification framework grounded in heterogeneous treatment effects, which integrates explicit and implicit mediation pathways via causal decomposition—enabling simultaneous identification of total, direct, and indirect effects without requiring multiple ignorability. The method combines flexible heterogeneity modeling, Monte Carlo simulation–based validation, and a dedicated software implementation. It is empirically validated in two real-world applications: public resource governance and voter information dissemination. Simulation studies demonstrate that, compared to prevailing approaches, the proposed method achieves substantially improved estimation accuracy and bias control. By relaxing strong untestable assumptions and offering practical implementation tools, this work advances causal mediation analysis with a more robust, interpretable, and accessible framework for uncovering underlying causal mechanisms.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Multiagent Systems: Mechanism Design

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Understanding causal mechanisms is crucial for explaining and generalizing empirical phenomena. Causal mediation analysis offers statistical techniques to quantify the mediation effects. However, current methods often require multiple ignorability assumptions or sophisticated research designs. In this paper, we introduce a novel identification strategy that enables the simultaneous identification and estimation of treatment and mediation effects. By combining explicit and implicit mediation analysis, this strategy exploits heterogeneous treatment effects through a new decomposition of total treatment effects. Monte Carlo simulations demonstrate that the method is more accurate and precise across various scenarios. To illustrate the efficiency and efficacy of our method, we apply it to estimate the causal mediation effects in two studies with distinct data structures, focusing on common pool resource governance and voting information. Additionally, we have developed statistical software to facilitate the implementation of our method.

Problem

• Identifies treatment and mediation effects simultaneously
• Leverages heterogeneous effects without unobserved confounders
• Applies method to governance and voting case studies

Innovation

• Combines explicit and implicit mediation analysis methods
• Leverages heterogeneous treatment effects for identification
• Avoids addressing certain unobserved confounders directly