Bounds on the fixed effects estimand in the presence of heterogeneous assignment propensities

Abstract Fixed effects estimation, with linear controls for stratum membership, is often used to estimate treatment effects when assignment propensities differ across strata. In the presence of heterogeneity in treatment effects across strata, this estimator does not target the average treatment effect, however. Indeed, the implied estimand can range anywhere from the lowest to the highest stratum-level average effect. To facilitate the interpretation of results using this approach, I establish that if stratum-level average effects are monotonic in the shares assigned to treatment, then the fixed effects estimand lies between the average treatment effect for the treated and the average treatment effect for the controls.

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Publication Details

Journal
Journal of Causal Inference
Published
2025-01-01
DOI
https://doi.org/10.1515/jci-2024-0040
Primary Topic
Advanced Causal Inference Techniques
Type
article
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article

Bounds on the fixed effects estimand in the presence of heterogeneous assignment propensities

Macartan Humphreys
Journal of Causal Inference
Advanced Causal Inference Techniques
article

Bounds on the fixed effects estimand in the presence of heterogeneous assignment propensities

Macartan Humphreys
article en

Abstract

Abstract Fixed effects estimation, with linear controls for stratum membership, is often used to estimate treatment effects when assignment propensities differ across strata. In the presence of heterogeneity in treatment effects across strata, this estimator does not target the average treatment effect, however. Indeed, the implied estimand can range anywhere from the lowest to the highest stratum-level average effect. To facilitate the interpretation of results using this approach, I establish that if stratum-level average effects are monotonic in the shares assigned to treatment, then the fixed effects estimand lies between the average treatment effect for the treated and the average treatment effect for the controls.

Journal of Causal InferenceVol. 13(1)
Trinity College Dublin (IE), WZB Berlin Social Science Center (DE), Humboldt-Universität zu Berlin (DE)
Good health and well-being
Openalex Percentile: Top 81%
Advanced Causal Inference Techniques
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