arXiv 4 Nov 2024 · Econometrics
arXiv:2411.02675 · PDF · DOI · OpenAlex · Extracted main text
We examine the challenges in ranking multiple treatments based on their estimated effects when using linear regression or its popular double-machine-learning variant, the Partially Linear Model (PLM), in the presence of treatment effect heterogeneity. We demonstrate by example that overlap-weighting performed by linear models like PLM can produce Weighted Average Treatment Effects (WATE) that have rankings that are inconsistent with the rankings of the underlying Average Treatment Effects (ATE). We define this as ranking reversals and derive a necessary and sufficient condition for ranking reversals under the PLM. We conclude with several simulation studies conditions under which ranking reversals occur.
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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, Victor, Chetverikov, Denis, Demirer, Mert, Duflo, Esth… (2018) Double/debiased machine learning for treatment and structural parameters | 0.644 | 2 | 2 | 100% |
| 2 | Angrist, Joshua D (1998) Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants | 0.405 | 1 | 1 | 100% |
| 3 | Angrist, Joshua D, Krueger, Alan B, Ashenfelter, Orley C, Card, David (1999) Chapter 23 - Empirical Strategies in Labor Economics | 0.405 | 1 | 1 | 100% |
| 4 | Aronow, Peter M, Samii, Cyrus (2016) Does Regression Produce Representative Estimates of Causal Effects? | 0.405 | 1 | 1 | 100% |
| 5 | Cattaneo, Matias D (2010) Efficient semiparametric estimation of multi-valued treatment effects under ignorability | 0.405 | 1 | 1 | 100% |
| 6 | Chernozhukov, Victor, Newey, Whitney K, Singh, Rahul (2022) Automatic Debiased Machine Learning of Causal and Structural Effects | 0.405 | 1 | 1 | 100% |
| 7 | Imbens, Guido W (2004) Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review | 0.405 | 1 | 1 | 100% |
| 8 | Słoczyński, Tymon (2022) Interpreting OLS Estimands when Treatment Effects are Heterogeneous | 0.405 | 1 | 1 | 100% |
Showing the top 8 of 8 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Does Residuals-on-Residuals Regression Produce Representative Estimates of Causal Effects? | 0.644 | 2 | 2 |
| 2 | Shrinkage-Based Regressions with Many Related Treatments | 0.405 | 1 | 1 |