A computational approach to identification of treatment effects for policy evaluation

Sukjin Han and Shenshen Yang

Journal of Econometrics, 2024, vol. 240, issue 1

Abstract: For counterfactual policy evaluation, it is important to ensure that treatment parameters are relevant to policies in question. This is especially challenging under unobserved heterogeneity, as is well featured in the definition of the local average treatment effect (LATE). Being intrinsically local, the LATE is known to lack external validity in counterfactual environments. This paper investigates the possibility of extrapolating local treatment effects to different counterfactual settings when instrumental variables can be only binary. We propose a novel framework to systematically calculate sharp nonparametric bounds on various policy-relevant treatment parameters that are defined as weighted averages of the marginal treatment effect (MTE). Our framework is flexible enough to fully incorporate statistical independence (rather than mean independence) of instruments and a large menu of identifying assumptions beyond the shape restrictions on the MTE that have been considered in prior studies. We apply our method to understand the effects of medical insurance policies on the use of medical services.

Keywords: Heterogeneous treatment effects; Local average treatment effects; Marginal treatment effects; Extrapolation; Partial identification; Linear programming (search for similar items in EconPapers)
JEL-codes: C14 C32 C33 C36 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Working Paper: A Computational Approach to Identification of Treatment Effects for Policy Evaluation (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:240:y:2024:i:1:s0304407624000265

DOI: 10.1016/j.jeconom.2024.105680

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