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

arXiv 7 Mar 2024 · Statistics — Methodology

arXiv:2403.04131 · PDF · DOI · OpenAlex · Extracted main text

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.

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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

	Reference	Intensity	Mentions	Sections	Main text
1	Fu, Jiawei and Slough, Tara (2026) Heterogeneous Treatment Effects and Causal Mechanisms self	0.928	4	3	100%
2	Blackwell, Matthew and Ma, Ruofan and Opacic, Aleksei (2024) Assumption Smuggling in Intermediate Outcome Tests of Causal Mechanisms	0.737	3	2	100%
3	Bowden, Jack and Davey Smith, George and Burgess, Stephen (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression	0.644	2	2	100%
4	Bullock, John G and Green, Donald P (2021) The failings of conventional mediation analysis and a design-based alternative	0.644	2	2	100%
5	Imai, Kosuke and Keele, Luke and Yamamoto, Teppei (2010) Identification, inference and sensitivity analysis for causal mediation effects	0.644	2	2	100%
6	Wager, Stefan and Athey, Susan (2018) Estimation and inference of heterogeneous treatment effects using random forests	0.644	2	2	100%
7	Slough, Tara and Rubenson, Daniel and Levy, Ro’ee and Alpizar Rodrig… (2021) Adoption of community monitoring improves common pool resource management across contexts	0.585	3	1	100%
8	Hong, Guanglei (2015) Causality in a social world: Moderation, mediation and spill-over	0.511	2	2	50%
9	Robins, James M and Greenland, Sander (1992) Identifiability and exchangeability for direct and indirect effects	0.511	2	2	50%
10	Sobel, Michael E (2008) Identification of causal parameters in randomized studies with mediating variables	0.511	2	2	50%

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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

	Citing paper	Intensity	Mentions	Sections
1	Heterogeneous Treatment Effects and Causal Mechanisms	0.405	1	1