Retrospective Causal Inference with Machine Learning Ensembles: An Application to Anti-recidivism Policies in Colombia

Cyrus Samii, Laura Paler and Sarah Zukerman Daly

Political Analysis, 2016, vol. 24, issue 4, 434-456

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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:cup:polals:v:24:y:2016:i:04:p:434-456_01

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