Retrospective causal inference with machine learning ensembles: An application to anti-recidivism policies in Colombia

Cyrus Samii, Laura Paler, Sarah Zukerman Daly

    Research output: Contribution to journalArticle

    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" 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.

    Original languageEnglish (US)
    Pages (from-to)434-456
    Number of pages23
    JournalPolitical Analysis
    Volume24
    Issue number4
    DOIs
    StatePublished - Oct 1 2016

    ASJC Scopus subject areas

    • Sociology and Political Science
    • Political Science and International Relations

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