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| DAG Causal Identification× | Mārkova ķēžu Montekarlo (MCMC)× | |
|---|---|---|
| Nozare≠ | Cēloņsakarību secināšana | Bajesa metodes |
| Saime≠ | Regression model | Bayesian methods |
| Izcelsmes gads≠ | 2009 | — |
| Autors≠ | Judea Pearl | — |
| Tips≠ | Causal identification framework | Posterior sampling algorithm |
| Pirmavots≠ | 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 |
| Citi nosaukumi≠ | do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus) | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | 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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