পদ্ধতির তুলনা করুন

	মেশিন লার্নিং-অগমেন্টেড প্রোপেনসিটি স্কোর ম্যাচিং	এন্ট্রপি ব্যালান্সিং
ক্ষেত্র	কার্যকারণ অনুমান	কার্যকারণ অনুমান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	2004	2012
প্রবর্তক	McCaffrey, Ridgeway & Morral (2004); Westreich, Lessler & Funk (2010)	Jens Hainmueller
ধরন	Causal inference / matching	Covariate-balancing reweighting
মৌলিক উৎস	McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9(4), 403-425.	Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.
অপর নাম	ML-PSM, boosted propensity score matching, ML-augmented PSM, nonparametric propensity score matching	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
সম্পর্কিত	6	6
সারসংক্ষেপ	Machine learning-augmented propensity score matching (ML-PSM) replaces the traditional logistic regression used to estimate propensity scores with flexible machine learning algorithms — such as gradient boosted trees, random forests, or LASSO — to better capture complex, nonlinear relationships among covariates. The resulting richer propensity scores improve covariate balance and reduce bias in the estimated average treatment effect on the treated (ATT).	Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step.