  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.
