Regression modelQuasi-experimental / causal inference

Multi-period Fuzzy Regression Discontinuity Design

Multi-period fuzzy regression discontinuity design estimates a local average treatment effect when a cutoff rule only partially determines treatment — that is, crossing the threshold raises the probability of treatment but does not guarantee it — and when this assignment process is observed across two or more time periods or cohorts, enabling pooled or period-specific causal estimates under repeated near-threshold comparisons.

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Sources

  1. Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design. Review of Economic Studies, 68(1), 201-209. DOI: 10.1111/1467-937X.00183
  2. Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2021). A Practical Introduction to Regression Discontinuity Designs: Extensions. Cambridge Elements in Quantitative and Computational Methods for the Social Sciences. Cambridge University Press. link

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ScholarGateMulti-period Fuzzy Regression Discontinuity (Multi-period Fuzzy Regression Discontinuity Design). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/multi-period-fuzzy-regression-discontinuity