Regression modelQuasi-experimental / causal inference

Heterogeneous Treatment Effect Regression Discontinuity Design (HTE-RDD)

Heterogeneous Treatment Effect RDD extends the classic regression discontinuity framework to detect and estimate how the causal effect of crossing an assignment cutoff varies across subgroups or along covariates. Rather than reporting a single local average treatment effect at the threshold, HTE-RDD maps how treatment impact differs by individual characteristics, enabling richer policy conclusions about who benefits most or least from a threshold-based intervention.

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Sources

  1. Dong, Y., & Lewbel, A. (2015). Identifying the Effect of Changing the Policy Threshold in Regression Discontinuity Models. Review of Economics and Statistics, 97(5), 1081-1092. DOI: 10.1162/REST_a_00510
  2. Chiang, H. D., Hsu, Y.-C., & Sasaki, Y. (2019). Causal Inference by Quantile Regression Kink Designs. Journal of Econometrics, 210(2), 405-433. DOI: 10.1016/j.jeconom.2019.01.009

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ScholarGateHeterogeneous Treatment Effect Regression Discontinuity Design (Heterogeneous Treatment Effect Regression Discontinuity Design). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/heterogeneous-treatment-effect-regression-discontinuity-design