Mean Reversion in Randomized Controlled Trials: Implications for Program Targeting and Heterogeneous Treatment Effects

Eligibility criteria for interventions can induce an Ashenfelter Dip, and subsequent mean reversion results in improvement over time even absent the intervention. We investigate these dynamics for a food-as-medicine program to treat diabetes, where eligibility required elevated hemoglobin A1c (HbA1c). Both treatment and control groups experienced significant improvements in HbA1c, resulting in an estimated null effect. When we predict improvement using baseline characteristics, we find that subjects who are unlikely to improve on their own appear to benefit from the program. Our findings have implications for program targeting and estimating heterogeneous treatment effects.

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Publication Details

Journal
AEA Papers and Proceedings
Published
2025-05-01
DOI
https://doi.org/10.1257/pandp.20251065
Citations
1
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
2.63
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article

Mean Reversion in Randomized Controlled Trials: Implications for Program Targeting and Heterogeneous Treatment Effects

Marcella Alsan, John Cawley, Nicholas Skelley, Joseph Doyle
1 citations
AEA Papers and Proceedings
Advanced Causal Inference Techniques
2.63
article

Mean Reversion in Randomized Controlled Trials: Implications for Program Targeting and Heterogeneous Treatment Effects

Marcella Alsan, John Cawley, Nicholas Skelley, Joseph Doyle
article en
1 citations

Abstract

Eligibility criteria for interventions can induce an Ashenfelter Dip, and subsequent mean reversion results in improvement over time even absent the intervention. We investigate these dynamics for a food-as-medicine program to treat diabetes, where eligibility required elevated hemoglobin A1c (HbA1c). Both treatment and control groups experienced significant improvements in HbA1c, resulting in an estimated null effect. When we predict improvement using baseline characteristics, we find that subjects who are unlikely to improve on their own appear to benefit from the program. Our findings have implications for program targeting and estimating heterogeneous treatment effects.

AEA Papers and ProceedingsVol. 115
Alfred P. Sloan Foundation (US), Harvard University (US), Cornell University (US)
Openalex Percentile: Top 11%
Advanced Causal Inference Techniques
2.63
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