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Heterogeneous Treatment Effects and Causal Mechanisms

Jiawei Fu, Tara Slough

arXiv 2 Apr 2024 · Econometrics

arXiv:2404.01566 · PDF · Extracted main text

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.

Citation extraction

47
references
97
in-text mentions
47
distinct cited
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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Slough, T. & Tyson, S. A (2024) External Validity and Evidence Accumulation self0.92843100%
2Anduiza, E., Gallego, A., & Muñoz, J (2013) Turning a blind eye: Experimental evidence of partisan bias in attitudes toward corruption0.8746467%
3Imai, K., Keele, L., & Tingley, D (2010) A general approach to causal mediation analysis0.8434375%
4de Figueiredo, M. F., Hidalgo, F., & Kasahara, Y (2023) When do voters punish corrupt politicians? experimental evidence from a field and survey experiment0.8435360%
5Egami, N. & Hartman, E (2022) Elements of external validity: Framework, design, and analysis0.84333100%
6Kitagawa, T. & Tetenov, A (2018) Who should be treated? empirical welfare maximization methods for treatment choice0.84333100%
7Eggers, A. C (2014) Partisanship and electoral accountability: Evidence from the uk expenses scandal0.8307457%
8Arias, E., Balán, P., Larreguy, H., Marshall, J., & Querubín, P (2019) Information provision, voter coordination, and electoral accountability: Evidence from mexican social networks0.7946350%
9Blackwell, M., Ma, R., & Opacic, A (2024) Assumption smuggling in intermediate outcome tests of causal mechanisms assumption smuggling in intermediate outcome tests of ca…0.73732100%
10Imai, K., Keele, L., Tingley, D., & Yamamoto, T (2011) Unpacking the black box of causality: Learning about causal mechanisms from experimental and observational studies0.73732100%

Showing the top 10 of 47 scored citations.

Cited by, within the corpus

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

Citing paperIntensityMentionsSections
1Extracting Mechanisms from Heterogeneous Effects: An Identification Strategy for Mediation Analysis0.92843
2A Formal Theory of Survey Experiment Generalizability: Attention and Salience0.40511