Linganisha mbinu

	Uzito wa Alama ya Mwelekeo (PSW / IPW)	Tofauti-katika-Tofauti (Diff-in-Diff)
Nyanja	Uhitimisho wa Kisababishi	Ekonometriki
Familia	Regression model	Regression model
Mwaka wa asili	1983 (propensity score); 2003 (efficient IPW estimator)	1994
Mwanzilishi	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Aina	Causal inference / reweighting	Causal inference / panel regression
Chanzo asilia	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.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Majina mbadala	PSW, inverse probability weighting, IPW, propensity-based weighting	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Zinazohusiana	6	5
Muhtasari	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).	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.