arXiv 28 Jun 2023 · Statistics — Methodology · publishedThe Annals of Applied Statistics (2025) · 11 citations (OpenAlex)
arXiv:2306.16591 · PDF · DOI · OpenAlex · Extracted main text
We introduce a new nonparametric causal decomposition approach that identifies the mechanisms by which a treatment variable contributes to a group-based outcome disparity. Our approach distinguishes three mechanisms: group differences in 1) treatment prevalence, 2) average treatment effects, and 3) selection into treatment based on individual-level treatment effects. Our approach reformulates classic Kitagawa-Blinder-Oaxaca decompositions in causal and nonparametric terms, complements causal mediation analysis by explaining group disparities instead of group effects, and isolates conceptually distinct mechanisms conflated in recent random equalization decompositions. In contrast to all prior approaches, our framework uniquely identifies differential selection into treatment as a novel disparity-generating mechanism. Our approach can be used for both the retrospective causal explanation of disparities and the prospective planning of interventions to change disparities. We present both an unconditional and a conditional decomposition, where the latter quantifies the contributions of the treatment within levels of certain covariates. We develop nonparametric estimators that are $\sqrt{n}$-consistent, asymptotically normal, semiparametrically efficient, and multiply robust. We apply our approach to analyze the mechanisms by which college graduation causally contributes to intergenerational income persistence (the disparity in adult income between the children of high- vs low-income parents). Empirically, we demonstrate a previously undiscovered role played by the new selection component in intergenerational income persistence.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Jackson, John W. and Tyler J. VanderWeele (2018) Decomposition Analysis to Identify Intervention Targets for Reducing Disparities: | 0.950 | 7 | 5 | 86% |
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| 7 | Brand, Jennie E. and Yu Xie (2010) Who Benefits Most from College?: Evidence for Negative Selection in Heterogeneous Economic Returns to Higher Education | 0.737 | 3 | 3 | 67% |
| 8 | Fortin, Nicole, Thomas Lemieux, and Sergio Firpo (2011) Decomposition Methods in Economics | 0.737 | 3 | 2 | 100% |
| 9 | Kennedy, Edward H (2022) Semiparametric doubly robust targeted double machine learning: a review | 0.644 | 4 | 2 | 50% |
| 10 | Brand, Jennie E., Jiahui Xu, Bernard Koch, and Pablo Geraldo (2021) Uncovering Sociological Effect Heterogeneity Using Tree-Based Machine Learning | 0.644 | 3 | 2 | 67% |
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| 1 | Heterogeneity Analysis with Heterogeneous Treatments | 0.511 | 2 | 1 |