পদ্ধতির তুলনা করুন

	শিক্ষা গবেষণায় ইনভার্স প্রোবাবিলিটি ওয়েটিং	ডিফারেন্স-ইন-ডিফারেন্সেস (ডিফ-ইন-ডিফ)
ক্ষেত্র	কার্যকারণ অনুমান	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1983–2003	1994
প্রবর্তক	Rosenbaum & Rubin (propensity score, 1983); Hirano, Imbens & Ridder (efficient IPW, 2003)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
ধরন	Causal weighting estimator	Causal inference / panel regression
মৌলিক উৎস	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
অপর নাম	IPW in education, propensity-weighted analysis, IPTW education, inverse probability treatment weighting	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
সম্পর্কিত	6	5
সারসংক্ষেপ	Inverse Probability Weighting (IPW) is a causal inference technique that reweights observational education data to mimic a randomised experiment. Each student or school is assigned a weight equal to the inverse of the probability they received the treatment — thereby creating a pseudo-population in which programme participation is independent of measured background characteristics. The method is widely used in education research to evaluate school programmes, interventions, and policies from administrative or survey data.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.