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Does Regression Produce Representative Causal Rankings?
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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.
Forward citations
Cited by 2 Pith papers
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Estimating Representative Causal Effects with Double Machine Learning
For continuous treatments, residuals-on-residuals regression targets a variance-weighted average of derivatives at unobserved pseudo-treatment values, not the average causal derivative; a coarsened AIPW estimator fixes this.
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Shrinkage-Based Regressions with Many Related Treatments
A customized ridge regression with an unpenalized focal treatment effect produces lower-variance estimates for many sparse sub-treatments and exactly recovers the single-treatment estimator.
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