Yöntem Karşılaştırma

	Regression Discontinuity in Policy Evaluation	Politika Değerlendirmesi İçin Kesintili Zaman Serileri
Alan	Public Policy	Nedensel çıkarım
Aile	Regression model	Regression model
Köken yılı	1960	1975 (intervention analysis); 2000s–2010s (policy evaluation framing)
Köken	Donald Thistlethwaite & Donald Campbell (design); Imbens, Lemieux, Lee (modern practice)	Box & Tiao (1975); popularised for policy by Shadish, Cook & Campbell (2002) and Bernal et al. (2017)
Tür	Quasi-experimental causal design for threshold-assigned policies	Quasi-experimental causal design
Seminal kaynak	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.	Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.
Diğer adlar	Policy RD Design, Threshold-Based Policy Evaluation, Cutoff Rule Evaluation, Eligibility-Threshold Design	ITS for policy evaluation, policy ITS, segmented regression for policy, policy impact ITS
İlişkili	3	4
Özet	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.	Interrupted Time Series (ITS) for policy evaluation uses routinely collected aggregate time-series data to estimate the causal impact of a policy change. A segmented regression model splits the series at a known intervention date, estimating both an immediate level shift and a change in trend attributable to the policy — without requiring a randomised control group.