Helmut Farbmacher, Martin Huber, Lukáš Lafférs, Henrika Langen, Martin Spindler
arXiv 28 Feb 2020 · Econometrics · publishedEconometrics Journal (2022) · 15 citations (OpenAlex)
arXiv:2002.12710 · PDF · DOI · OpenAlex · Extracted main text
This paper combines causal mediation analysis with double machine learning to control for observed confounders in a data-driven way under a selection-on-observables assumption in a high-dimensional setting. We consider the average indirect effect of a binary treatment operating through an intermediate variable (or mediator) on the causal path between the treatment and the outcome, as well as the unmediated direct effect. Estimation is based on efficient score functions, which possess a multiple robustness property w.r.t. misspecifications of the outcome, mediator, and treatment models. This property is key for selecting these models by double machine learning, which is combined with data splitting to prevent overfitting in the estimation of the effects of interest. We demonstrate that the direct and indirect effect estimators are asymptotically normal and root-n consistent under specific regularity conditions and investigate the finite sample properties of the suggested methods in a simulation study when considering lasso as machine learner. We also provide an empirical application to the U.S. National Longitudinal Survey of Youth, assessing the indirect effect of health insurance coverage on general health operating via routine checkups as mediator, as well as the direct effect. We find a moderate short term effect of health insurance coverage on general health which is, however, not mediated by routine checkups.
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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 | Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 14 | 3 | 100% |
| 2 | Tchetgen Tchetgen and Shpitser (2012) Semiparametric theory for causal mediation analysis: Efficiency bounds, multiple robustness, and sensitivity analysis | 1.000 | 13 | 6 | 100% |
| 3 | Pearl (2001) Direct and indirect effects | 1.000 | 5 | 3 | 100% |
| 4 | Huber (2014) Identifying causal mechanisms (primarily) based on inverse probability weighting self | 0.737 | 3 | 2 | 100% |
| 5 | Robins and Greenland (1992) Identifiability and Exchangeability for Direct and Indirect Effects | 0.737 | 3 | 2 | 100% |
| 6 | Flores and Flores-Lagunes (2009) Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment under Unconfoundedness | 0.644 | 2 | 2 | 100% |
| 7 | Hong (2010) Ratio of mediator probability weighting for estimating natural direct and indirect effects | 0.644 | 2 | 2 | 100% |
| 8 | Imai, Keele, and Yamamoto (2010) Identification, Inference and Sensitivity Analysis for Causal Mediation Effects | 0.644 | 2 | 2 | 100% |
| 9 | Petersen, Sinisi, and van der Laan (2006) Estimation of Direct Causal Effects | 0.644 | 2 | 2 | 100% |
| 10 | Robins, Rotnitzky, and Zhao (1994) Estimation of Regression Coefficients When Some Regressors Are not Always Observed | 0.644 | 2 | 2 | 100% |
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