So sánh phương pháp

	Mô hình hiệu ứng ngẫu nhiên dữ liệu bảng	Phương pháp Biến Công cụ (IV) cho Suy luận Nhân quả
Lĩnh vực	Kinh tế lượng	Kinh tế học y tế
Họ	Regression model	Process / pipeline
Năm ra đời	2021	1990s (modern applications)
Người khởi xướng	Baltagi (textbook treatment); classical random-effects panel estimator	Angrist & Pischke (applied econometrics); rooted in econometric theory
Loại	Panel data regression	Method
Công trình gốc	Baltagi, B. H. (2021). Econometric Analysis of Panel Data (6th ed.). Springer.	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Tên gọi khác	random effects panel model, RE estimator, GLS random effects, Panel Veri — Rassal Etkiler Modeli	IV, two-stage least squares, TSLS, causal estimation
Liên quan	5	3
Tóm tắt	The Random Effects model is a panel-data regression that treats unobserved individual heterogeneity as a random component drawn from a common distribution, rather than a separate parameter for each unit. It is a standard estimator in panel econometrics, developed in textbook treatments such as Baltagi's Econometric Analysis of Panel Data (2021).	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.