| Autor: |
Samii, Cyrus, Paler, Laura, Daly, Sarah Zukerman |
| Rok vydání: |
2016 |
| Předmět: |
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| Zdroj: |
Political Analysis (2016) |
| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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