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1. Verfasser: Lal, Apoorva
Format: Preprint
Veröffentlicht: 2024
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Online-Zugang:https://arxiv.org/abs/2411.02675
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author Lal, Apoorva
author_facet Lal, Apoorva
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2411_02675
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Does Regression Produce Representative Causal Rankings?
Lal, Apoorva
Econometrics
Methodology
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.
title Does Regression Produce Representative Causal Rankings?
topic Econometrics
Methodology
url https://arxiv.org/abs/2411.02675