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Reverse matching for ex-ante policy evaluation

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  • George Planiteros

Abstract

The paper attacks the central policy evaluation question of forecasting the impact of interventions never previously experienced. It introduces treatment effects approach into a cognitive domain not currently spanned by its methodological arsenal. Existing causal effects bounding analysis is adjusted to the ex-ante program evaluation setting. A Monte Carlo experiment is conducted to test how severe the estimates of the proposed approach deviate from the "real" causal effect in the presence of selection and unobserved heterogeneity. The simulation shows that the approach is valid regarding the formulation of the counterfactual states given previous knowledge of the program rules and a sufficiently informative treatment probability. It also demonstrates that the width of the bounds are resilient to several deviations from the conditional independence assumption.

Suggested Citation

  • George Planiteros, 2022. "Reverse matching for ex-ante policy evaluation," DEOS Working Papers 2206, Athens University of Economics and Business.
    • Handle: RePEc:aue:wpaper:2206
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    References listed on IDEAS

    as
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