Retrospective Causal Inference with Machine Learning Ensembles: An Application to Anti-Recidivism Policies in Colombia
This work addresses the challenge of retrospective causal inference for policymakers and researchers, offering a novel approach to improve accuracy in evaluating anti-recidivism policies, though it appears incremental as it builds on existing ensemble methods.
The authors tackled the problem of estimating causal effects retrospectively from micro data by developing a method that uses a machine learning ensemble to address limitations in conventional approaches like regression or matching, and applied it to analyze policy options for reducing ex-combatant recidivism in Colombia.
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'' (RIE) 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.