arXiv 30 Jan 2022 · Econometrics
arXiv:2201.12752 · PDF · DOI · OpenAlex · Extracted main text
Empirical researchers are often interested in not only whether a treatment affects an outcome of interest, but also how the treatment effect arises. Causal mediation analysis provides a formal framework to identify causal mechanisms through which a treatment affects an outcome. The most popular identification strategy relies on so-called sequential ignorability (SI) assumption which requires that there is no unobserved confounder that lies in the causal paths between the treatment and the outcome. Despite its popularity, such assumption is deemed to be too strong in many settings as it excludes the existence of unobserved confounders. This limitation has inspired recent literature to consider an alternative identification strategy based on an instrumental variable (IV). This paper discusses the identification of causal mediation effects in a setting with a binary treatment and a binary instrumental variable that is both assumed to be random. We show that while IV methods allow for the possible existence of unobserved confounders, additional monotonicity assumptions are required unless the strong constant effect is assumed. Furthermore, even when such monotonicity assumptions are satisfied, IV estimands are not necessarily equivalent to target parameters.
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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 | Kosuke Imai, Luke Keele, and Teppei Yamamoto (2010) Identification, inference and sensitivity analysis for causal mediation effects | 0.737 | 3 | 2 | 100% |
| 2 | Guido W. Imbens and Joshua D. Angrist (1994) Identification and estimation of local average treatment effects | 0.644 | 4 | 1 | 100% |
| 3 | Judea Pearl (2001) Direct and indirect effects | 0.585 | 3 | 1 | 100% |
| 4 | Joshua D. Angrist and Guido W. Imbens (1995) Two-stage least squares estimation of average causal effects in models with variable treatment intensity | 0.511 | 2 | 1 | 100% |
| 5 | Stacey H. Chen, Yen-Chien Chen, and Jin-Tan Liu (2019) The impact of family composition on educational achievement | 0.405 | 1 | 1 | 100% |
| 6 | Christian Dippel, Gold Robert, Heblich Stephan, and Rodrigo Pinto (2020) Mediation analysis in iv settings with a single instrument | 0.405 | 1 | 1 | 100% |
| 7 | Christian Dippel, Gold Robert, Heblich Stephan, and Pinto Rodrigo (2021) The effect of trade on workers and voters | 0.405 | 1 | 1 | 100% |
| 8 | James Heckman, Rodrigo Pinto, and Peter Savelyev (2013) Understanding the mechanisms through which an influential early childhood program boosted adult outcomes | 0.405 | 1 | 1 | 100% |
| 9 | Markus Frölich and Martin Huber (2017) Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables | 0.405 | 1 | 1 | 100% |
| 10 | James J. Heckman (2001) Micro data, heterogeneity, and the evaluation of public policy: Nobel lecture | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 13 scored citations.