Does Regression Produce Representative Causal Rankings?

2024/11/04 by Apoorva Lal, Lal, Apoorva · 1 voice · 2 citations
Economics, Econometrics and Finance · Mathematics · Social Sciences · #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Methodology (stat.ME) #Qualitative Comparative Analysis Research #econ.EM #stat.ME

paper · pdf · doi:10.48550/arxiv.2411.02675

openalex publication_date 2024/11/04 · arxiv published 2024/11/04 · arxiv updated 2024/11/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• Shrinkage-Based Regressions with Many Related Treatments
• Estimating Representative Causal Effects with Double Machine Learning

Discussions

• That paper is about something even more basic; naively regression estimates aren't even internally valid. (Building on that- rankings are borked arxiv.org/abs/2411.02675) I agree that doesn't bode wel [bsky, 2 points, 1 comments]

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