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A bootstrap procedure to estimate the causal effect of a public policy, considering overlap and imperfect compliance

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  • Stefano Cabras

Abstract

This paper introduces a nonparametric bootstrap method for estimating the causal effects of public policy under the circumstances of imperfect compliance and overlap. It focuses on business investment subsidies in Sardinia by comparing firms eligible for the 1999 subsidies to those not, amid issues of imperfect compliance and overlapping programs. Bootstrap confidence intervals (CI) are proposed for the average effect of treatment on the sub-population of compliers. The obtained CIs are consistent across nominal levels and robust against data nonnormality; they show coverages of credible intervals close to nominal, suggesting effectiveness for assessing causal effects. Compared to other methods, the results of the new combination of a specific estimator for incompliance and the bootstrap align with those of more modern approaches such as Bayesian Additive Regression Trees and Causal forest.

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  • Stefano Cabras, 2025. "A bootstrap procedure to estimate the causal effect of a public policy, considering overlap and imperfect compliance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 52(7), pages 1470-1484, May.
    • Handle: RePEc:taf:japsta:v:52:y:2025:i:7:p:1470-1484
    DOI: 10.1080/02664763.2024.2428994
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