Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Kausaalinen identifiointi suunnatuilla syklittömillä graafeilla (do-calculus)× | OLS-regressio (Ordinary Least Squares)× | |
|---|---|---|
| Tieteenala≠ | Kausaalipäättely | Ekonometria |
| Menetelmäperhe | Regression model | Regression model |
| Syntyvuosi≠ | 2009 | 2019 |
| Kehittäjä≠ | Judea Pearl | Wooldridge (textbook treatment); classical least squares |
| Tyyppi≠ | Causal identification framework | Linear regression |
| Alkuperäislähde≠ | 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 |
| Rinnakkaisnimet | 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 |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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). |
| ScholarGateAineisto ↗ |
|
|