Zobrazeno 1 - 10
of 71
pro vyhledávání: '"do‐calculus"'
Publikováno v:
Advanced Science, Vol 11, Iss 46, Pp n/a-n/a (2024)
Abstract Quantifying molecular regulations between genes/molecules causally from observed data is crucial for elucidating the molecular mechanisms underlying biological processes at the network level. Presently, most methods for inferring gene regula
Externí odkaz:
https://doaj.org/article/fa894eca3c404335abd246cd72892dca
Publikováno v:
Human-Centric Intelligent Systems, Vol 4, Iss 2, Pp 286-298 (2024)
Abstract Bayesian networks are commonly used for learning with uncertainty and incorporating expert knowledge. However, they are hard to interpret, especially when the network structure is complex. Methods used to explain Bayesian networks operate un
Externí odkaz:
https://doaj.org/article/84ab2dde3c984a9e9a1daac1f22202e0
Publikováno v:
Journal of Statistics and Data Science Education, Pp 1-15 (2022)
We present an instructional approach to teaching causal inference using Bayesian networks and do-Calculus, which requires less prerequisite knowledge of statistics than existing approaches and can be consistently implemented in beginner to advanced l
Externí odkaz:
https://doaj.org/article/c5a10583a0e44d96aca2557ff3aa9fa4
Akademický článek
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Autor:
Pearl Judea
Publikováno v:
Journal of Causal Inference, Vol 10, Iss 1, Pp 221-226 (2022)
In a recent issue of this journal, Philip Dawid (2021) proposes a framework for causal inference that is based on statistical decision theory and that is, in many aspects, compatible with the familiar framework of causal graphs (e.g., Directed Acycli
Externí odkaz:
https://doaj.org/article/daf982489111410ba578c64d5b43b84b
Publikováno v:
IEEE Access, Vol 10, Pp 24327-24339 (2022)
A high-stakes decision requires deep thought to understand the complex factors that stop a situation from becoming worse. Such decisions are carried out under high pressure, with a lack of information, and in limited time. This research applies Causa
Externí odkaz:
https://doaj.org/article/5b4de778a9af47448aa6cc7f729a5797
Publikováno v:
Journal of Causal Inference, Vol 9, Iss 1, Pp 211-228 (2021)
The estimation of causal effects is fundamental in situations where the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functi
Externí odkaz:
https://doaj.org/article/de11996ecb704087b408031298968d60
Publikováno v:
Journal of Statistical Software, Vol 99, Iss 1 (2021)
Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system. While complete g
Externí odkaz:
https://doaj.org/article/fb20f4ae0bd3465bb2a0b4b54592f0a4
Autor:
Santtu Tikka, Juha Karvanen
Publikováno v:
Journal of Statistical Software, Vol 76, Iss 1, Pp 1-30 (2017)
Do-calculus is concerned with estimating the interventional distribution of an action from the observed joint probability distribution of the variables in a given causal structure. All identifiable causal effects can be derived using the rules of do-
Externí odkaz:
https://doaj.org/article/f0ab3a627fb646669cb9880769de32b3
Autor:
Pearl Judea
Publikováno v:
Journal of Causal Inference, Vol 3, Iss 1, Pp 131-137 (2015)
In this issue of the Causal, Casual, and Curious column, I compare several ways of extracting information from post-treatment variables and call attention to some peculiar relationships among them. In particular, I contrast do-calculus conditioning w
Externí odkaz:
https://doaj.org/article/ad2222c4fa704904aca41b10179c0f06