Retrospective Causal Inference with Machine Learning Ensembles: An   Application to Anti-Recidivism Policies 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. 
Cyrus Samii, Laura Paler and Sarah Zukerman Daly
stat.ML