Retrospective Causal Inference with Machine Learning Ensembles: An Application to Anti-recidivism Policies in Colombia. (2016)

• Record Type: Journal Article
• Authors: Samii, Cyrus; Paler, Laura; Daly, Sarah Zukerman
• Is Part Of: Political analysis. Volume 24:Number 4(2016:Winter)
• Page Start: 434
• Page End: 456
• Publication Date: 2016
• Subjects: Political science -- Methodology -- Periodicals; Electronic journals; 320.011
• DOI: 10.1093/pan/mpw019
• Languages: English
• ISSNs: 1047-1987

Abstract

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.