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Direct and Indirect Effects based on Changes-in-Changes

Martin Huber, Mark Schelker, Anthony Strittmatter

arXiv 11 Sep 2019 · Econometrics

arXiv:1909.04981 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We propose a novel approach for causal mediation analysis based on changes-in-changes assumptions restricting unobserved heterogeneity over time. This allows disentangling the causal effect of a binary treatment on a continuous outcome into an indirect effect operating through a binary intermediate variable (called mediator) and a direct effect running via other causal mechanisms. We identify average and quantile direct and indirect effects for various subgroups under the condition that the outcome is monotonic in the unobserved heterogeneity and that the distribution of the latter does not change over time conditional on the treatment and the mediator. We also provide a simulation study and an empirical application to the Jobs II programme.

Citation extraction

61
references
97
in-text mentions
61
distinct cited
5
self-citations
15,831
main-text words

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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
1Athey and Imbens (2006) Identification and Inference in Nonlinear Difference-In-Difference Models1.000103100%
2Deuchert, Huber, and Schelker (2017) Direct and indirect effects based on difference-in-differences with an application to political preferences following the Vietna… self1.00073100%
3Angrist, Imbens, and Rubin (1996) Identification of Causal Effects Using Instrumental Variables0.92843100%
4Vinokur, Price, and Schul (1995) Impact of the JOBS Intervention on Unemployed Workers Varying in Risk for Depression0.87472100%
5Sawada (2019) Non-Compliance in Randomized Control Trials without Exclusion Restrictions0.69351100%
6Chaisemartin and D'Haultfeuille (2018) Fuzzy Differences-in-Differences0.64441100%
7Frangakis and Rubin (2002) Principal Stratification in Causal Inference0.64422100%
8Caplan, Vinokur, Price, and van Ryn (1989) Job seeking, reemployment, and mental health: A randomized field experiment in coping with job loss0.51121100%
9Imbens and Angrist (1994) Identification and Estimation of Local Average Treatment Effects0.51121100%
10Robins and Greenland (1992) Identifiability and Exchangeability for Direct and Indirect Effects0.51121100%

Showing the top 10 of 61 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
1Identifying causal channels of policy reforms with multiple treatments and different types of selection0.40511
2Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning0.40511
3On a Debiased and Semiparametric Efficient Changes-in-Changes Estimator0.40511
4Difference-in-Differences with a Mediator0.40511