Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Машинне навчання для оцінки контрфактичного впливу× | Контрфактуативна оцінка впливу (КОВ)× | |
|---|---|---|
| Галузь | Причинно-наслідковий висновок | Причинно-наслідковий висновок |
| Родина | Regression model | Regression model |
| Рік появи≠ | 2016-2019 | 1970s–2000s |
| Автор методу≠ | Chernozhukov et al.; Athey & Imbens | Heckman, Imbens, Rubin, and the program evaluation literature |
| Тип≠ | Causal inference / ML-augmented evaluation | Causal inference / program evaluation |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви | ML-augmented counterfactual evaluation, ML-CIE, causal ML impact evaluation, double ML counterfactual evaluation | CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation |
| Пов'язані | 5 | 5 |
| Підсумок≠ | 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. |
| ScholarGateНабір даних ↗ |
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