Сравнение на методи

	Оценка с машинно обучение и двойна робастност (ML-DR)	Метод на разликите в разликите (Difference-in-Differences, DiD)
Област	Причинно-следствено заключение	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	2018	1994
Създател	Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey & Robins	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Тип	Semiparametric causal estimator with ML nuisance	Causal inference / panel regression
Основополагащ източник	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
Други названия	ML-DR, AIPW with ML, Double/Debiased ML doubly robust, DML-DR	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Свързани	6	5
Резюме	Machine learning-augmented doubly robust (ML-DR) estimation combines the classical doubly robust (AIPW) identification strategy with flexible machine learning models for the nuisance functions — the propensity score and the outcome regression. The result is a causal estimator that is consistent if either ML component is correctly specified, and that achieves valid, root-n inference even when the nuisance models are estimated with high-dimensional regularisation or nonparametric learners.	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.