So sánh phương pháp
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| Phân tích Trung tâm× | Phương pháp Biến Công cụ (IV) cho Suy luận Nhân quả× | |
|---|---|---|
| Lĩnh vực≠ | Phân tích mạng lưới | Kinh tế học y tế |
| Họ | Process / pipeline | Process / pipeline |
| Năm ra đời≠ | 1979 | 1990s (modern applications) |
| Người khởi xướng≠ | Linton C. Freeman | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Loại≠ | Descriptive / exploratory network measure family | Method |
| Công trình gốc≠ | Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| Tên gọi khác | Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality | IV, two-stage least squares, TSLS, causal estimation |
| Liên quan≠ | 5 | 3 |
| Tóm tắt≠ | Centrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies nodes that broker information flow, closeness centrality captures proximity to all others, and eigenvector centrality (along with PageRank) rewards connection to highly connected neighbors. | 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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