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Heterogeneous Treatment Effects and Causal Mechanisms

Jiawei Fu & Tara Slough

American Political Science Review2026https://doi.org/10.1017/s0003055426101580preprint
AJG 4*ABDC A*
Weight
0.50

Abstract

The credibility revolution advances the use of research designs that permit the identification and estimation of causal effects. However, understanding which mechanisms produce measured causal effects remains a challenge. The dominant current approach to the quantitative evaluation of mechanisms relies on the detection of heterogeneous treatment effects (HTEs) with respect to pretreatment covariates. This article develops a framework to understand when the existence of such HTEs can support inferences about the activation of a mechanism. We show first that this design cannot provide evidence of mechanism activation without additional, generally implicit, exclusion assumptions. Further, even when these assumptions are satisfied, the presence of HTEs supports the inference that the mechanism is active but the absence of HTEs is generally uninformative about mechanism activation. We provide novel guidance for interpretation and research design in light of these findings.

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https://doi.org/https://doi.org/10.1017/s0003055426101580

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@article{jiawei2026,
  title        = {{Heterogeneous Treatment Effects and Causal Mechanisms}},
  author       = {Jiawei Fu & Tara Slough},
  journal      = {American Political Science Review},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1017/s0003055426101580},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.