方法证据记录
Machine learning-augmented regression discontinuity design
Machine learning-augmented regression discontinuity design (ML-RDD) combines the sharp identification logic of classical RDD — exploiting a known assignment cutoff in a running variable — with flexible, data-adaptive ML methods for bandwidth selection, conditional mean estimation, and covariate adjustment. The goal is to recover a more accurate and less assumption-laden estimate of the local average treatment effect at the threshold.
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 Regression Discontinuity Design
分类方法记录 · regression-model / causal-inference
- Calonico, S., Cattaneo, M. D., & Farrell, M. H. (2019). Optimal mean squared error bandwidth selection for regression discontinuity designs. Bernoulli, 25(4A), 2703-2729. · URL
- Imbens, G., & Wager, S. (2019). Optimized regression discontinuity designs. Review of Economics and Statistics, 101(2), 264-278. · DOI 10.1162/rest_a_00793
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