Porównaj metody

	Uczenie maszynowe wspomagające ewaluację wpływu kontrfaktycznego	Metoda różnic w różnicach (Diff-in-Diff)
Dziedzina	Wnioskowanie przyczynowe	Ekonometria
Rodzina	Regression model	Regression model
Rok powstania	2016-2019	1994
Twórca	Chernozhukov et al.; Athey & Imbens	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Typ	Causal inference / ML-augmented evaluation	Causal inference / panel regression
Źródło pierwotne	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.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Inne nazwy	ML-augmented counterfactual evaluation, ML-CIE, causal ML impact evaluation, double ML counterfactual evaluation	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Pokrewne	5	5
Podsumowanie	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).	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.