Knowledge-Bridged Causal Interaction Network for Causal Emotion Entailment

Causal Knowledge Network Integration for Life Cycle Assessment

Sustainability requires emphasizing the importance of environmental causes and effects among design knowledge from heterogeneous stakeholders to make a sustainable decision. Recently, such causes and effects have been well developed in ontological representation, which has been challenged to generate and integrate multiple domain knowledge due to its domain specific characteristics. Moreover, i...

Causal Product Knowledge Management

Analytic Causal Knowledge for Constructing Useable Empirical Causal Knowledge: Two Experiments on Preschoolers

The present paper examines what domain-general causal knowledge reasoners need for at least some outcome-variable types to construct useable content-specific causal knowledge. In particular, it explains why it is essential to have analytic knowledge of causal-invariance integration functions: knowledge for predicting the expected outcome assuming that the empirical knowledge acquired regarding ...

Causal inference and causal explanation with background knowledge

This paper presents correct algorithms for answering the following two questions; (i) Does there exist a causal explanation con­ sistent with a set of background knowledge which explains all of the observed indepen­ dence facts in a sample? (ii) Given that there is such a causal explanation what are the causal relationships common to every such

Causal Networks Learning Acausal Networks Learning Influence Diagrams Learning Causal-Network Parameters Learning Causal-Network Structure Learning Hidden Variables Learning More General Causal Models Advances: Learning Causal Networks

Bayesian methods have been developed for learning Bayesian networks from data. Most of this work has concentrated on Bayesian networks interpreted as a representation of probabilistic conditional independence without considering causation. Other researchers have shown that having a causal interpretation can be important, because it allows us to predict the effects of interventions in a domain. ...