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

TL;DR

This paper introduces a new machine learning ensemble method for retrospective causal inference, addressing limitations of traditional approaches by better handling large covariate sets and clarifying counterfactuals, demonstrated through anti-recidivism policy analysis in Colombia.

Contribution

It develops a novel ensemble-based approach for retrospective causal inference that improves accuracy and interpretability over conventional methods.

Findings

Effective estimation of the retrospective intervention effect (RIE)

Improved policy analysis for reducing recidivism in Colombia

Demonstrates robustness with large covariate sets

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'' (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.