পদ্ধতির তুলনা করুন

	ডাইনামিক ইনভার্স প্রোবাবিলিটি ওয়েটিং	প্রোপেনসিটি স্কোর ওয়েটিং (PSW / IPW)
ক্ষেত্র	কার্যকারণ অনুমান	কার্যকারণ অনুমান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1986-2000	1983 (propensity score); 2003 (efficient IPW estimator)
প্রবর্তক	James M. Robins and colleagues	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
ধরন	Causal weighting estimator	Causal inference / reweighting
মৌলিক উৎস	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	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	Dynamic IPW, Time-varying IPW, Longitudinal IPW, Sequential IPW	PSW, inverse probability weighting, IPW, propensity-based weighting
সম্পর্কিত	4	6
সারসংক্ষেপ	Dynamic Inverse Probability Weighting (Dynamic IPW) estimates the causal effect of a time-varying treatment sequence by reweighting observed data to mimic a hypothetical randomised trial. Developed by Robins and colleagues in the context of marginal structural models, it handles the challenge that in longitudinal settings, past treatment affects future covariates, which in turn affect future treatment — a feedback loop that standard regression cannot untangle.	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).