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Machine learning-augmented regression discontinuity design/Evidence
Method evidence record

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.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Machine Learning-Augmented Regression Discontinuity Design
Taxonomic method record · 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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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Taxonomic bucketFuzzy Regression Discontinuitymachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMachine learning-augmented difference-in-differencesmachine-suggested · Relational suggestion, not evidence.See alsoPropensity Score Matchingmachine-suggested · Relational suggestion, not evidence.

Evidence status

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Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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