Methoden vergleichen

	Maschinelles Lernen-augmentierte Propensity Score Gewichtung	Differenz-in-Differenzen (DiD)
Fachgebiet	Kausale Inferenz	Ökonometrie
Familie	Regression model	Regression model
Entstehungsjahr	2010–2018	1994
Urheber	Lee, Lessler & Stuart (2010); Chernozhukov et al. (2018, DML framework)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Typ	Causal inference / semiparametric weighting	Causal inference / panel regression
Wegweisende Quelle	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
Aliasnamen	ML-PSW, ML-augmented IPW, machine learning propensity weighting, nonparametric propensity score weighting	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Verwandt	5	5
Zusammenfassung	Machine learning-augmented propensity score weighting (ML-PSW) replaces logistic regression with flexible ML algorithms — such as gradient boosting, LASSO, or random forests — to estimate the propensity score, then uses inverse probability weights to balance treated and control groups. This reduces model-misspecification bias when the true relationship between covariates and treatment assignment is complex or high-dimensional.	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.