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	मशीन लर्निंग-संवर्धित रिग्रेशन डिसकंटीन्यूटी डिज़ाइन	प्रोपेंसिटी स्कोर मैचिंग
क्षेत्र	कारणात्मक अनुमान	अनुसंधान सांख्यिकी
परिवार	Regression model	Process / pipeline
उद्भव वर्ष	2019	1983
प्रवर्तक	Imbens & Wager (2019); Calonico, Cattaneo & Farrell (2019)	Paul Rosenbaum and Donald Rubin
प्रकार	Causal inference / quasi-experimental	Method
मौलिक स्रोत	Calonico, S., Cattaneo, M. D., & Farrell, M. H. (2019). Optimal mean squared error bandwidth selection for regression discontinuity designs. Bernoulli, 25(4A), 2703-2729.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
उपनाम	ML-RDD, ML-augmented RD, data-adaptive RDD, nonparametric RDD with ML	PSM, propensity score weighting, covariate balance
संबंधित	3	3
सारांश	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.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.