方法证据记录
Machine Learning-Augmented Fuzzy Regression Discontinuity
ML-augmented fuzzy RDD extends the classical fuzzy regression discontinuity design by replacing parametric polynomial approximations with flexible machine learning estimators. Where standard fuzzy RDD uses IV-style estimation at a threshold with imperfect compliance, the ML-augmented variant leverages nonparametric learners — such as random forests or neural networks — to model both the outcome and the first-stage treatment probability near the cutoff, reducing misspecification bias while preserving causal identification.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Machine Learning-Augmented Fuzzy Regression Discontinuity Design
分类方法记录 · regression-model / causal-inference
- 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 10.1111/1468-0262.00183
- Semenova, V., & Chernozhukov, V. (2021). Debiased machine learning of conditional average treatment effects and other causal functions. The Econometrics Journal, 24(2), 264-289. · DOI 10.1093/ectj/utaa027
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