Regression model
Regression Discontinuity Design (RDD)
Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.
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Sources
- Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635. DOI: 10.1016/j.jeconom.2007.05.001 ↗
- Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2020). A Practical Introduction to Regression Discontinuity Designs: Foundations. Cambridge University Press. ISBN: 978-1108710206
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Referenced by
Conditional Process AnalysisDifference-in-DiscontinuitiesEvent Study DesignFrontdoor AdjustmentHeterogeneous Treatment EffectsInterrupted Time SeriesInverse Probability Weighting in Education ResearchLocal Average Treatment EffectMachine Learning-Augmented Sensitivity Analysis for CausalityMarginal structural model in education researchMendelian RandomizationPlacebo TestsPolicy Evaluation Event Study DesignPolicy Evaluation Matching EstimatorPolicy Evaluation Panel Event StudyPropensity Score Weighting in Education ResearchRegression Kink DesignSensitivity analysis for causality in education researchShift-Share IVStaggered Difference-in-DifferencesSynthetic Control