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 language | English (US) |
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Pages (from-to) | 434-456 |
Number of pages | 23 |
Journal | Political Analysis |
Volume | 24 |
Issue number | 4 |
DOIs | |
State | Published - Oct 1 2016 |
ASJC Scopus subject areas
- Sociology and Political Science
- Political Science and International Relations