Salīdzināt metodes

	Politikas novērtēšana ar tendenču rezultātu saskaņošanu	Diferenču starpībām (Diff-in-Diff)
Nozare	Cēloņsakarību secināšana	Ekonometrija
Saime	Regression model	Regression model
Izcelsmes gads	1983; policy evaluation adaptation 1997	1994
Autors	Rosenbaum & Rubin (1983); Heckman, Ichimura & Todd (1997) for program/policy evaluation application	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tips	Quasi-experimental matching estimator	Causal inference / panel regression
Pirmavots	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Citi nosaukumi	PSM policy evaluation, policy PSM, propensity matching for program evaluation, PSM treatment evaluation	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Saistītās	6	5
Kopsavilkums	Policy evaluation propensity score matching applies the propensity score framework — originally developed by Rosenbaum and Rubin (1983) and operationalized for program evaluation by Heckman et al. (1997) — to estimate the causal effect of a policy intervention. It constructs a credible comparison group from non-participants by matching them to participants on their estimated probability of receiving the treatment, enabling unbiased effect estimation without random assignment.	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.