পদ্ধতির তুলনা করুন

	বিষমধর্মী ট্রিটমেন্ট ইফেক্ট প্রপেনসিটি স্কোর ম্যাচিং	প্রোপেনসিটি স্কোর ম্যাচিং (Propensity Score Matching)
ক্ষেত্র	কার্যকারণ অনুমান	গবেষণা পরিসংখ্যান
পরিবার	Regression model	Process / pipeline
উদ্ভবের বছর	1983–2016	1983
প্রবর্তক	Rosenbaum & Rubin (PSM foundation, 1983); Athey & Imbens (HTE extensions, 2016)	Paul Rosenbaum and Donald Rubin
ধরন	Causal inference / matching with effect heterogeneity	Method
মৌলিক উৎস	Athey, S., & Imbens, G. W. (2016). Recursive Partitioning for Heterogeneous Causal Effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360.	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	HTE-PSM, CATE via PSM, subgroup treatment effect matching, conditional average treatment effect matching	PSM, propensity score weighting, covariate balance
সম্পর্কিত	5	3
সারসংক্ষেপ	Heterogeneous Treatment Effect Propensity Score Matching extends standard PSM to estimate how treatment effects vary across subgroups or individual characteristics. Rather than reporting a single average treatment effect, it uses the matched sample to estimate conditional average treatment effects (CATE), revealing which types of units benefit most or least from a treatment.	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.