Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Плацебо-тести для причинно-наслідкового висновку× | DAG Causal Identification× | |
|---|---|---|
| Галузь | Причинно-наслідковий висновок | Причинно-наслідковий висновок |
| Родина | Regression model | Regression model |
| Рік появи≠ | 2010 | 2009 |
| Автор методу≠ | Abadie, Diamond & Hainmueller (synthetic control placebos); Imbens & Lemieux (RDD validity) | Judea Pearl |
| Тип≠ | Falsification / robustness test family for causal inference | Causal identification framework |
| Основоположне джерело≠ | 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 ↗ | Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606 |
| Інші назви | falsification tests, placebo checks, refutation tests, Plasebo Testleri — Nedensel Çıkarım Doğrulama | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) |
| Пов'язані | 5 | 5 |
| Підсумок≠ | Placebo tests are a family of falsification checks that probe the credibility of a causal claim by re-running the analysis on a fake treatment, a false intervention date, or an outcome that should not have been affected. The approach was popularised through the synthetic control work of Abadie, Diamond and Hainmueller (2010) and the regression-discontinuity validity checks of Imbens and Lemieux (2008). | DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths. |
| ScholarGateНабір даних ↗ |
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