Nonparametric Treatment Effect Identification in School Choice

Jiafeng Chen

Papers from arXiv.org

Abstract: This paper studies nonparametric identification and estimation of causal effects in centralized school assignment. In many centralized assignment algorithms, students are subjected to both lottery-driven variation and regression discontinuity (RD) driven variation. We characterize the full set of identified atomic treatment effects (aTEs), defined as the conditional average treatment effect between a pair of schools, given student characteristics. Atomic treatment effects are the building blocks of more aggregated notions of treatment contrasts, and common approaches to estimating aggregations of aTEs can mask important heterogeneity. In particular, many aggregations of aTEs put zero weight on aTEs driven by RD variation, and estimators of such aggregations put asymptotically vanishing weight on the RD-driven aTEs. We provide a diagnostic and recommend new aggregation schemes. Lastly, we provide estimators and accompanying asymptotic results for inference for those aggregations.

Date: 2021-12, Revised 2025-07
New Economics Papers: this item is included in nep-dcm, nep-des, nep-ecm and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2112.03872

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