Linganisha mbinu

	Regression Discontinuity in Policy Evaluation	Tathmini ya Sera Muundo wa Kukatizwa kwa Regresi
Nyanja	Public Policy	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	1960	1960; policy evaluation applications widespread from 2000s
Mwanzilishi	Donald Thistlethwaite & Donald Campbell (design); Imbens, Lemieux, Lee (modern practice)	Thistlethwaite & Campbell (1960); popularized in policy evaluation by Lee & Lemieux (2010)
Aina	Quasi-experimental causal design for threshold-assigned policies	Quasi-experimental causal design
Chanzo asilia	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.	Lee, D. S., & Lemieux, T. (2010). Regression Discontinuity Designs in Economics. Journal of Economic Literature, 48(2), 281-355.
Majina mbadala	Policy RD Design, Threshold-Based Policy Evaluation, Cutoff Rule Evaluation, Eligibility-Threshold Design	Policy RDD, RD design in policy evaluation, regression discontinuity policy analysis, RDD policy impact
Zinazohusiana	3	5
Muhtasari	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 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.