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| Phương pháp Kiểm soát Tổng hợp để Đánh giá Chính sách× | Phương pháp Biến Công cụ (IV) cho Suy luận Nhân quả× | |
|---|---|---|
| Lĩnh vực≠ | Suy luận nhân quả | Kinh tế học y tế |
| Họ≠ | Regression model | Process / pipeline |
| Năm ra đời≠ | 2003-2010 | 1990s (modern applications) |
| Người khởi xướng≠ | Alberto Abadie & Javier Gardeazabal; extended by Abadie, Diamond & Hainmueller | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Loại≠ | Causal inference / comparative case study | Method |
| Công trình gốc≠ | Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| Tên gọi khác | Synthetic Control Method, SCM, Synthetic Control, Abadie-Diamond-Hainmueller method | IV, two-stage least squares, TSLS, causal estimation |
| Liên quan≠ | 5 | 3 |
| Tóm tắt≠ | The Synthetic Control Method (SCM) is a causal inference technique for evaluating the effect of a policy or intervention on a single treated unit — such as a region, country, or firm — by constructing a weighted combination of untreated comparison units that closely mirrors the treated unit before the intervention. Introduced by Abadie and Gardeazabal (2003) and formalized by Abadie, Diamond, and Hainmueller (2010), it provides a data-driven, transparent counterfactual for comparative case studies. | 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. |
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