Módszerek összehasonlítása

	Heterogén kezelési hatás becslése illesztett propensity score-val	A különbség-különbségek (Diff-in-Diff) módszer
Tudományterület	Oksági következtetés	Ökonometria
Módszercsalád	Regression model	Regression model
Keletkezés éve	1983–2016	1994
Megalkotó	Rosenbaum & Rubin (PSM foundation, 1983); Athey & Imbens (HTE extensions, 2016)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Típus	Causal inference / matching with effect heterogeneity	Causal inference / panel regression
Alapmű	Athey, S., & Imbens, G. W. (2016). Recursive Partitioning for Heterogeneous Causal Effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alternatív nevek	HTE-PSM, CATE via PSM, subgroup treatment effect matching, conditional average treatment effect matching	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Kapcsolódó	5	5
Összefoglaló	Heterogeneous Treatment Effect Propensity Score Matching extends standard PSM to estimate how treatment effects vary across subgroups or individual characteristics. Rather than reporting a single average treatment effect, it uses the matched sample to estimate conditional average treatment effects (CATE), revealing which types of units benefit most or least from a treatment.	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.