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	DAG Causal Identification	प्रोपेंसिटी स्कोर मैचिंग
क्षेत्र	कारणात्मक अनुमान	अनुसंधान सांख्यिकी
परिवार	Regression model	Process / pipeline
उद्भव वर्ष	2009	1983
प्रवर्तक	Judea Pearl	Paul Rosenbaum and Donald Rubin
प्रकार	Causal identification framework	Method
मौलिक स्रोत	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606	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	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	PSM, propensity score weighting, covariate balance
संबंधित	5	3
सारांश	DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths.	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.