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| DAG (Directed Acyclic Graph) による因果推論特定 (do-calculus)× | 最小二乗法 (OLS) 回帰× | |
|---|---|---|
| 分野≠ | 因果推論 | 計量経済学 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 2009 | 2019 |
| 提唱者≠ | Judea Pearl | Wooldridge (textbook treatment); classical least squares |
| 種類≠ | Causal identification framework | Linear regression |
| 原典≠ | Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 別名 | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 関連 | 5 | 5 |
| 概要≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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