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

Published online by Cambridge University Press:  04 January 2017

Cyrus Samii ,
Laura Paler  and
Sarah Zukerman Daly
Cyrus Samii*
Affiliation:
Department of Politics, New York University, 19 West 14th Street, New York, NY 10012
Laura Paler
Affiliation:
Department of Political Science, University of Pittsburgh, 4600 Wesley W. Posvar Hall, Pittsburgh, PA 15260 e-mail: lpaler@pitt.edu
Sarah Zukerman Daly
Affiliation:
Department of Political Science, University of Notre Dame, 217 O’Shaughnessy Hall, Notre Dame, IN 46556 e-mail: sarahdaly@nd.edu
*
e-mail: cds2083@nyu.edu
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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.

Type
Articles
Information
Political Analysis , Volume 24 , Issue 4 , Autumn 2016 , pp. 434 - 456
Copyright
Copyright © The Author 2016. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors’ note: Authors are listed in reverse alphabetical order and are equal contributors to the project. All replication materials are available at the Political Analysis Dataverse (article url: http://dx.doi.org/10.7910/DVN/QXCFO2). We thank Carolina Serrano for excellent research assistance in Colombia and the team at Fundación Ideas para la Paz for their collaboration in the data collection. We also thank the Organization of American States, Misión de Apoyo al Proceso de Paz and the Agencia Colombiana para la Reintegración for their collaboration. Daly acknowledges funding from the Swedish Foreign Ministry, the Smith Richardson Foundation, and the Carroll L. Wilson Award. For helpful discussions, the authors thank Michael Alvarez, two anonymous Political Analysis reviewers, Deniz Aksoy, Peter Aronow, Neal Beck, Matthew Blackwell, Drew Dimmery, Ryan Jablonski, Michael Peress, Fredrik Savje, Maya Sen, Teppei Yamamoto, Rodrigo Zarazaga, and seminar participants at the American Political Science Association annual meetings, European Political Science Association annual meetings, Empirical Studies of Conflict working group, Massachussetts Institute of Technology, Midwest Political Science association annual meetings, New York University, and the University of Rochester. Supplementary materials for this article are available on the Political Analysis website.

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