Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Masinõppimisega täiustatud kontrafaktuaalne mõjuanalüüs× | Kausaalne mõju analüüs× | |
|---|---|---|
| Valdkond | Põhjuslik järeldamine | Põhjuslik järeldamine |
| Perekond | Regression model | Regression model |
| Tekkeaasta≠ | 2016-2019 | 2015 |
| Looja≠ | Chernozhukov et al.; Athey & Imbens | Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google) |
| Tüüp≠ | Causal inference / ML-augmented evaluation | Bayesian causal inference / counterfactual forecasting |
| Algallikas≠ | 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 ↗ | Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274. DOI ↗ |
| Rööpnimetused | ML-augmented counterfactual evaluation, ML-CIE, causal ML impact evaluation, double ML counterfactual evaluation | CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | 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). | Causal Impact Analysis, introduced by Brodersen et al. (2015) at Google, uses Bayesian structural time-series models to estimate what would have happened to an outcome had an intervention never occurred. By constructing a probabilistic counterfactual from pre-treatment data and control covariates, it quantifies point-in-time and cumulative treatment effects with full posterior uncertainty intervals. |
| ScholarGateAndmestik ↗ |
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