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	Policy Evaluation Regression Discontinuity Design	Policyutvärdering med Difference-in-Differences
Ämnesområde	Kausal inferens	Kausal inferens
Familj	Regression model	Regression model
Ursprungsår	1960; policy evaluation applications widespread from 2000s	1978-2009
Upphovsperson	Thistlethwaite & Campbell (1960); popularized in policy evaluation by Lee & Lemieux (2010)	Ashenfelter (1978); Heckman, LaLonde & Smith (1999); Imbens & Wooldridge (2009)
Typ	Quasi-experimental causal design	Quasi-experimental / policy evaluation
Ursprungskälla	Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.	Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.
Alias	Policy RDD, RD design in policy evaluation, regression discontinuity policy analysis, RDD policy impact	policy DiD, program evaluation DiD, policy impact DiD, DiD policy assessment
Närliggande	5	4
Sammanfattning	Policy Evaluation Regression Discontinuity Design (Policy RDD) exploits a known eligibility threshold in a policy rule to estimate the causal effect of that policy on outcomes. Units just below the cutoff serve as a credible comparison group for units just above it, making RDD one of the most transparent quasi-experimental strategies for assessing what a policy actually achieves.	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.