পদ্ধতির তুলনা করুন

	বেয়েশীয়ান এক্স পোস্ট ফ্যাক্টো ডিজাইন	প্রোপেনসিটি স্কোর ম্যাচিং (Propensity Score Matching)
ক্ষেত্র	গবেষণা নকশা	গবেষণা পরিসংখ্যান
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1964 (Kerlinger ex post facto); Bayesian integration from 1990s–2000s onward	1983
প্রবর্তক	Frederick N. Kerlinger (ex post facto framework); Bayesian extension draws on Laplace and modern Bayesian statistics	Paul Rosenbaum and Donald Rubin
ধরন	Quantitative observational research design with Bayesian inference	Method
মৌলিক উৎস	Kerlinger, F. N. (1973). Foundations of Behavioral Research (2nd ed.). Holt, Rinehart and Winston.	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.
অপর নাম	Bayesian causal-comparative design, Bayesian after-the-fact design, Bayesian observational causal design, Bayesian retrospective causal study	PSM, propensity score weighting, covariate balance
সম্পর্কিত	5	3
সারসংক্ষেপ	Bayesian ex post facto design investigates possible causal relationships among variables that have already occurred, without researcher manipulation of those variables, and quantifies uncertainty about those relationships using Bayesian statistical inference. The researcher selects groups that differ on an outcome or a presumed cause after the fact, then uses prior knowledge and observed data together — via Bayes' theorem — to estimate credible effect sizes, group differences, or predictors.	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.