Linganisha mbinu

	Mtafsiri wa Makadirio ya Mechi ya Tathmini ya Sera	Uzito wa Kinyume wa Uwezekano wa Matibabu (IPW / IPTW)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	1998-2006	2000
Mwanzilishi	Heckman, Ichimura & Todd; Abadie & Imbens	Robins, Hernán & Brumback
Aina	Non-parametric causal estimator	Causal inference weighting estimator
Chanzo asilia	Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Majina mbadala	matching estimator, program evaluation matching, treatment effect matching, Abadie-Imbens estimator	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Zinazohusiana	6	5
Muhtasari	The policy evaluation matching estimator estimates the causal effect of a program or policy on treated units by pairing each participant with one or more non-participants who share similar pre-treatment characteristics. Developed rigorously by Heckman, Ichimura & Todd (1998) and Abadie & Imbens (2006), it avoids parametric outcome models and is the standard non-parametric tool for program and policy evaluation.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.