Módszerek összehasonlítása

	Regression Discontinuity in Policy Evaluation	Politikai értékelés különbségek különbségek módszerrel
Tudományterület	Public Policy	Oksági következtetés
Módszercsalád	Regression model	Regression model
Keletkezés éve	1960	1978-2009
Megalkotó	Donald Thistlethwaite & Donald Campbell (design); Imbens, Lemieux, Lee (modern practice)	Ashenfelter (1978); Heckman, LaLonde & Smith (1999); Imbens & Wooldridge (2009)
Típus	Quasi-experimental causal design for threshold-assigned policies	Quasi-experimental / policy evaluation
Alapmű	Thistlethwaite, D. L., & Campbell, D. T. (1960). Regression-discontinuity analysis: An alternative to the ex post facto experiment. Journal of Educational Psychology, 51(6), 309–317.	Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.
Alternatív nevek	Policy RD Design, Threshold-Based Policy Evaluation, Cutoff Rule Evaluation, Eligibility-Threshold Design	policy DiD, program evaluation DiD, policy impact DiD, DiD policy assessment
Kapcsolódó	3	4
Összefoglaló	Regression discontinuity (RD) is a quasi-experimental design for estimating the causal effect of a policy that is assigned by a sharp threshold on some continuous eligibility score — an income line for a benefit, a test score for a scholarship, a vote share for winning office, a population cutoff that triggers a regulation. Units falling just below and just above the cutoff are nearly identical except for their treatment status, so comparing their outcomes isolates the policy's effect at the threshold. First used by Thistlethwaite and Campbell in 1960 and revived as a workhorse of policy evaluation by economists in the 2000s, RD is widely regarded as the quasi-experimental design with the strongest claim to internal validity.	Policy Evaluation DiD applies the difference-in-differences estimator specifically to assess the causal impact of government programs, regulations, or policy reforms. It compares outcome changes in a group exposed to the policy against a comparable untreated group, before and after the policy took effect, isolating the net policy effect from pre-existing trends and time-common shocks.