পদ্ধতির তুলনা করুন

	ডাইনামিক ম্যাচিং এস্টিমেটর	প্রোপেনসিটি স্কোর ম্যাচিং (Propensity Score Matching)
ক্ষেত্র	কার্যকারণ অনুমান	গবেষণা পরিসংখ্যান
পরিবার	Regression model	Process / pipeline
উদ্ভবের বছর	2010	1983
প্রবর্তক	Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998)	Paul Rosenbaum and Donald Rubin
ধরন	Nonparametric causal inference / matching	Method
মৌলিক উৎস	Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137.	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 treatment matching, sequential matching estimator, dynamic selection-on-observables, DME	PSM, propensity score weighting, covariate balance
সম্পর্কিত	6	3
সারসংক্ষেপ	The Dynamic Matching Estimator extends standard matching methods to settings where treatment is assigned sequentially over multiple periods. Instead of a single treatment decision, units receive or forgo treatment at each time point, and the estimator identifies causal effects of entire treatment histories by matching on time-varying covariates and past treatment paths, under sequential conditional independence assumptions.	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.