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| Analisis Sensitiviti Bayesian untuk Kausaliti× | Kaedah Pemboleh Ubah Instrumental (IV) untuk Inferensi Kausal× | |
|---|---|---|
| Bidang≠ | Inferens Kausal | Ekonomi Kesihatan |
| Keluarga≠ | Regression model | Process / pipeline |
| Tahun asal≠ | 2000s–2010s | 1990s (modern applications) |
| Pengasas≠ | McCandless, Gustafson & Austin (2007); Gustafson (2015) | Angrist & Pischke (applied econometrics); rooted in econometric theory |
| Jenis≠ | Bayesian causal sensitivity analysis | Method |
| Sumber perintis≠ | McCandless, L. C., Gustafson, P., & Austin, P. C. (2007). Bayesian propensity score analysis for observational data. Statistics in Medicine, 26(8), 1704-1718. DOI ↗ | Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press. link ↗ |
| Alias | Bayesian sensitivity analysis, Bayesian bias analysis, probabilistic sensitivity analysis for confounding, Bayesian unmeasured confounding analysis | IV, two-stage least squares, TSLS, causal estimation |
| Berkaitan≠ | 6 | 3 |
| Ringkasan≠ | Bayesian sensitivity analysis for causality quantifies how much an unmeasured confounder would need to influence both treatment assignment and outcome to overturn a causal conclusion. Rather than testing a single worst-case scenario, it places prior distributions over the strength of hidden confounding, propagates uncertainty through a full Bayesian model, and reports a posterior distribution for the causal effect that honestly reflects what is and is not identified from observed 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. |
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