arXiv 12 Feb 2020 · Econometrics · publishedEconometric Reviews (2022) · 3 citations (OpenAlex)
arXiv:2002.05253 · PDF · DOI · OpenAlex · Extracted main text
Causal mediation analysis aims at disentangling a treatment effect into an indirect mechanism operating through an intermediate outcome or mediator, as well as the direct effect of the treatment on the outcome of interest. However, the evaluation of direct and indirect effects is frequently complicated by non-ignorable selection into the treatment and/or mediator, even after controlling for observables, as well as sample selection/outcome attrition. We propose a method for bounding direct and indirect effects in the presence of such complications using a method that is based on a sequence of linear programming problems. Considering inverse probability weighting by propensity scores, we compute the weights that would yield identification in the absence of complications and perturb them by an entropy parameter reflecting a specific amount of propensity score misspecification to set-identify the effects of interest. We apply our method to data from the National Longitudinal Survey of Youth 1979 to derive bounds on the explained and unexplained components of a gender wage gap decomposition that is likely prone to non-ignorable mediator selection and outcome attrition.
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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 | Huber and Solovyeva (2018) Direct and indirect effects under sample selection and outcome attrition | 0.874 | 6 | 2 | 100% |
| 2 | Hong, Qin, and Yang (2018) Weighting-Based Sensitivity Analysis in Causal Mediation Studies | 0.874 | 5 | 2 | 100% |
| 3 | Huber and Solovyeva (2020) On the sensitivity of wage gap decompositions | 0.874 | 5 | 2 | 100% |
| 4 | Robins and Greenland (1992) Identifiability and Exchangeability for Direct and Indirect Effects | 0.737 | 3 | 2 | 100% |
| 5 | Heckman (1979) Sample Selection Bias as a Specification Error | 0.644 | 2 | 2 | 100% |
| 6 | Pearl (2001) Direct and indirect effects | 0.644 | 2 | 2 | 100% |
| 7 | Robins (2003) Semantics of causal DAG models and the identification of direct and indirect effects | 0.644 | 2 | 2 | 100% |
| 8 | Rubin (1976) Inference and Missing Data | 0.644 | 2 | 2 | 100% |
| 9 | Lafférs and Nedela (2017) Sensitivity of the bounds on the ATE in the presence of sample selection | 0.644 | 2 | 2 | 100% |
| 10 | Albert and Nelson (2011) Generalized causal mediation analysis | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 53 scored citations.