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Heterogeneous Treatment Effects and Causal Mechanisms

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  • Jiawei Fu
  • Tara Slough

Abstract

The credibility revolution advances the use of research designs that permit identification and estimation of causal effects. However, understanding which mechanisms produce measured causal effects remains a challenge. A dominant current approach to the quantitative evaluation of mechanisms relies on the detection of heterogeneous treatment effects with respect to pre-treatment covariates. This paper develops a framework to understand when the existence of such heterogeneous treatment effects can support inferences about the activation of a mechanism. We show first that this design cannot provide evidence of mechanism activation without an additional, generally implicit, assumption. Further, even when this assumption is satisfied, if a measured outcome is produced by a non-linear transformation of a directly-affected outcome of theoretical interest, heterogeneous treatment effects are not informative of mechanism activation. We provide novel guidance for interpretation and research design in light of these findings.

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  • Jiawei Fu & Tara Slough, 2024. "Heterogeneous Treatment Effects and Causal Mechanisms," Papers 2404.01566, arXiv.org, revised Jun 2024.
    • Handle: RePEc:arx:papers:2404.01566
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    References listed on IDEAS

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    1. Toru Kitagawa & Aleksey Tetenov, 2018. "Who Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice," Econometrica, Econometric Society, vol. 86(2), pages 591-616, March.
    2. Strezhnev, Anton & Kelley, Judith G. & Simmons, Beth A., 2021. "Testing for Negative Spillovers: Is Promoting Human Rights Really Part of the “Problem”?," International Organization, Cambridge University Press, vol. 75(1), pages 71-102, January.
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