قارن الطرق

	التعرف السببي باستخدام الرسوم البيانية الموجهة غير الدورية (حسابات do)	مطابقة درجات الميل
المجال	الاستدلال السببي	إحصاء البحث
العائلة	Regression model	Process / pipeline
سنة النشأة	2009	1983
صاحب الطريقة	Judea Pearl	Paul Rosenbaum and Donald Rubin
النوع	Causal identification framework	Method
المصدر التأسيسي	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.
الأسماء البديلة	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	PSM, propensity score weighting, covariate balance
ذات صلة	5	3
الملخص	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.