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| Kausal identifikation med rettede acykliske grafer (do-calculus)× | Almindelig mindste kvadraters metode (OLS) regression× | |
|---|---|---|
| Fagområde≠ | Kausal inferens | Økonometri |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 2009 | 2019 |
| Ophavsperson≠ | Judea Pearl | Wooldridge (textbook treatment); classical least squares |
| Type≠ | Causal identification framework | Linear regression |
| Oprindelig kilde≠ | 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 |
| Aliasser | 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 |
| Relaterede | 5 | 5 |
| Resumé≠ | 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). |
| ScholarGateDatasæt ↗ |
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