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| Kausaalinen identifiointi suunnatuilla syklittömillä graafeilla (do-calculus)× | Markov-ketju-Monte Carlo (MCMC)× | |
|---|---|---|
| Tieteenala≠ | Kausaalipäättely | Bayesilainen tilastotiede |
| Menetelmäperhe≠ | Regression model | Bayesian methods |
| Syntyvuosi≠ | 2009 | — |
| Kehittäjä≠ | Judea Pearl | — |
| Tyyppi≠ | Causal identification framework | Posterior sampling algorithm |
| Alkuperäislähde≠ | Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606 | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| Rinnakkaisnimet≠ | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Liittyvät≠ | 5 | 3 |
| 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. | Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model. |
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