Bandingkan kaedah

	Penimbang Kebolehjadian Songsang dalam Penyelidikan Pendidikan	Kaedah Pemboleh Ubah Instrumental (IV) untuk Inferensi Kausal
Bidang	Inferens Kausal	Ekonomi Kesihatan
Keluarga	Regression model	Process / pipeline
Tahun asal	1983–2003	1990s (modern applications)
Pengasas	Rosenbaum & Rubin (propensity score, 1983); Hirano, Imbens & Ridder (efficient IPW, 2003)	Angrist & Pischke (applied econometrics); rooted in econometric theory
Jenis	Causal weighting estimator	Method
Sumber perintis	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: Princeton University Press.
Alias	IPW in education, propensity-weighted analysis, IPTW education, inverse probability treatment weighting	IV, two-stage least squares, TSLS, causal estimation
Berkaitan	6	3
Ringkasan	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.	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.