Jiawei Fu, Tara Slough
arXiv 2 Apr 2024 · Econometrics
arXiv:2404.01566 · PDF · Extracted main text
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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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 | Slough, T. & Tyson, S. A (2024) External Validity and Evidence Accumulation self | 0.928 | 4 | 3 | 100% |
| 2 | Anduiza, E., Gallego, A., & Muñoz, J (2013) Turning a blind eye: Experimental evidence of partisan bias in attitudes toward corruption | 0.874 | 6 | 4 | 67% |
| 3 | Imai, K., Keele, L., & Tingley, D (2010) A general approach to causal mediation analysis | 0.843 | 4 | 3 | 75% |
| 4 | de Figueiredo, M. F., Hidalgo, F., & Kasahara, Y (2023) When do voters punish corrupt politicians? experimental evidence from a field and survey experiment | 0.843 | 5 | 3 | 60% |
| 5 | Egami, N. & Hartman, E (2022) Elements of external validity: Framework, design, and analysis | 0.843 | 3 | 3 | 100% |
| 6 | Kitagawa, T. & Tetenov, A (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 0.843 | 3 | 3 | 100% |
| 7 | Eggers, A. C (2014) Partisanship and electoral accountability: Evidence from the uk expenses scandal | 0.830 | 7 | 4 | 57% |
| 8 | Arias, E., Balán, P., Larreguy, H., Marshall, J., & Querubín, P (2019) Information provision, voter coordination, and electoral accountability: Evidence from mexican social networks | 0.794 | 6 | 3 | 50% |
| 9 | Blackwell, M., Ma, R., & Opacic, A (2024) Assumption smuggling in intermediate outcome tests of causal mechanisms assumption smuggling in intermediate outcome tests of ca… | 0.737 | 3 | 2 | 100% |
| 10 | Imai, K., Keele, L., Tingley, D., & Yamamoto, T (2011) Unpacking the black box of causality: Learning about causal mechanisms from experimental and observational studies | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 47 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Extracting Mechanisms from Heterogeneous Effects: An Identification Strategy for Mediation Analysis | 0.928 | 4 | 3 |
| 2 | A Formal Theory of Survey Experiment Generalizability: Attention and Salience | 0.405 | 1 | 1 |