retrospective causal inference with machine learning ensembles: an application to anti-recidivism policies in colombia

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Authors Cyrus Samii, Laura Paler, Sarah Zukerman Daly
Journal/Conference Name POLITICAL ANALYSIS
Paper Category
Paper Abstract We present new methods to estimate causal effects retrospectively from micro data with the assistance of a machine learning ensemble. This approach overcomes two important limitations in conventional methods like regression modeling or matching: (i) ambiguity about the pertinent retrospective counterfactuals and (ii) potential misspecification, overfitting, and otherwise bias-prone or inefficient use of a large identifying covariate set in the estimation of causal effects. Our method targets the analysis toward a well defined "retrospective intervention effect" (RIE) based on hypothetical population interventions and applies a machine learning ensemble that allows data to guide us, in a controlled fashion, on how to use a large identifying covariate set. We illustrate with an analysis of policy options for reducing ex-combatant recidivism in Colombia.
Date of publication 2016
Code Programming Language R
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