Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Evaluarea contrafactuală a impactului augmentată prin învățare automată× | Evaluarea Contrafactuală a Impactului (CIE)× | |
|---|---|---|
| Domeniu | Inferență cauzală | Inferență cauzală |
| Familie | Regression model | Regression model |
| Anul apariției≠ | 2016-2019 | 1970s–2000s |
| Autorul original≠ | Chernozhukov et al.; Athey & Imbens | Heckman, Imbens, Rubin, and the program evaluation literature |
| Tip≠ | Causal inference / ML-augmented evaluation | Causal inference / program evaluation |
| Sursa seminală≠ | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68. DOI ↗ | Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. Handbook of Econometrics, 6B, 4779-4874. DOI ↗ |
| Denumiri alternative | ML-augmented counterfactual evaluation, ML-CIE, causal ML impact evaluation, double ML counterfactual evaluation | CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation |
| Înrudite | 5 | 5 |
| Rezumat≠ | Machine learning-augmented counterfactual impact evaluation combines the credibility of potential-outcomes causal inference with the flexibility of modern ML algorithms. Rather than imposing parametric functional forms for confounders, ML learners — such as lasso, random forests, or neural nets — estimate nuisance functions (propensity scores, outcome regressions) that are then used to construct approximately unbiased estimates of causal effects. The canonical instantiation is Double/Debiased Machine Learning (DML), formalized by Chernozhukov et al. (2018). | Counterfactual Impact Evaluation is a family of causal methods that estimates the effect of an intervention by comparing what actually happened to participants with what would have happened had the intervention not taken place. Formalised in the Rubin Causal Model and extended by Heckman, Imbens and others, CIE underlies most modern program and policy evaluation practice. |
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