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Search for Causal Models
Published in Marloes Maathuis, Mathias Drton, Steffen Lauritzen, Martin Wainwright, Handbook of Graphical Models, 2018
A causal graph is a directed graph over a set of random variables V in which there is a directed edge from A to B if and only if A is a direct cause of B relative to V [77]. Equivalently, in terms of manipulations, in a causal graph there is a directed edge from A to B if and only if there is some pair of experimental manipulations of the set of variables in V\{B} $ \mathbf V \setminus \{B\} $ that differ only in how A is manipulated that changes the probability distribution of B. See Chapter for more details. A set of variables V is causally sufficient when every direct (relative to V) cause of two variables in V is also in V (i.e. there are no unmeasured confounders.)
Modeling Causal-Effect Relationships for Artificial Intelligence Applications
Published in Don Potter, Manton Matthews, Moonis Ali, Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 2020
Causal models are further subdivided into explicit and implicit causal models. Explicit causal models use a graph of nodes and links to depict cause-effect knowledge explicitly. This can be used for a number of tasks such as diagnosis, explanations, etc. The nodes of a causal graph could represent states, events, actions, tendencies, and state changes [Rei76]. The links could represent causes, possibly causes, etc. Implicit causal models use structures that do not represent causality explicitly but can be used to predict the behavior or produce explanations of a system.
A causal inference method for canal safety anomaly detection based on structural causal model and GBDT
Published in LHB, 2023
Hairui Li, Xuemei Liu, Xianfeng Huai, Xiaolu Chen
A causal graph is a directed acyclic graph that consists of nodes and directed edges, where the former represent variables and the latter represent the form of causal effects between variables. In Figure 3, the causal relationship between explanatory variables and output variables is represented by a solid line, while the causal relationship between unobserved variables and output variables is represented by a dashed line. The causal graph provides a convenient tool for describing complex causal relationships in a system, and it also reveals the challenge faced in the causal inference process, namely the potential confounding bias in the statistical model constructed when there is a back-door path between the variables to be inferred. ‘Back-door path’ refers to the presence of confounding variables that together affect the explanatory variable x with the output variable y in the analysis of .
Towards Responsible AI: A Design Space Exploration of Human-Centered Artificial Intelligence User Interfaces to Investigate Fairness
Published in International Journal of Human–Computer Interaction, 2023
Yuri Nakao, Lorenzo Strappelli, Simone Stumpf, Aisha Naseer, Daniele Regoli, Giulia Del Gamba
The main way for a user to explore features and relationships between features, a key need that was highlighted in the workshops, is through a causal graph (Figure 8) (REQ2, REQ3). The causal graph is made up of nodes representing features and edges representing causal relationships between features, presented in a circular layout. Causal graphs can be inferred by applying causal discovery algorithms (Zheng et al., 2018) and then followed up and validated by domain experts. Sensitive features marked up during the setup process are highlighted in gold (REQ1.5); relationship strengths are indicated by line thickness (REQ3.2); out-degree is represented by node size. The target is shown as a circle node to differentiate it from the other features.
General power laws of the causalities in the causal Bayesian networks
Published in International Journal of General Systems, 2023
Boyuan Li, Xiaoyang Li, Zhaoxing Tian, Xia Lu, Rui Kang
A causal graph is a Directed Acyclic Graph (DAG), in which nodes represent factors, and the directed arrows between nodes represent the causal relationships from causes to effects, as shown in Figure 1 (a). The causal graph is also referred to the causal Bayesian network (CBN).