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Adaptive Estimation of Aggregated Values of Conditional Linear Programs

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  • Gevorg Khandamiryan
  • Vira Semenova

Abstract

We develop a covariate-assisted approach to partially identified parameters that are solutions to an under-identified system of linear equations with known coefficients. Examples include bounds on treatment effects, models of unemployment with state dependence, choice-theoretic models of IV, and random utility models. The boundary (i.e., support function) of the proposed identified set is represented as an average of intersections of regression functions, aggregated over the covariate distribution. We show that the boundary is a regular parameter, propose asymptotic theory, and demonstrate using an empirical application to Jobs First.

Suggested Citation

  • Gevorg Khandamiryan & Vira Semenova, 2026. "Adaptive Estimation of Aggregated Values of Conditional Linear Programs," Papers 2606.08359, arXiv.org.
    • Handle: RePEc:arx:papers:2606.08359
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    References listed on IDEAS

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