Log In Sign Up

Learning Causal Bayesian Networks from Text

by   Farhad Moghimifar, et al.

Causal relationships form the basis for reasoning and decision-making in Artificial Intelligence systems. To exploit the large volume of textual data available today, the automatic discovery of causal relationships from text has emerged as a significant challenge in recent years. Existing approaches in this realm are limited to the extraction of low-level relations among individual events. To overcome the limitations of the existing approaches, in this paper, we propose a method for automatic inference of causal relationships from human written language at conceptual level. To this end, we leverage the characteristics of hierarchy of concepts and linguistic variables created from text, and represent the extracted causal relationships in the form of a Causal Bayesian Network. Our experiments demonstrate superiority of our approach over the existing approaches in inferring complex causal reasoning from the text.


Using Noisy Extractions to Discover Causal Knowledge

Knowledge bases (KB) constructed through information extraction from tex...

Causal Reasoning for Algorithmic Fairness

In this work, we argue for the importance of causal reasoning in creatin...

BARD: A structured technique for group elicitation of Bayesian networks to support analytic reasoning

In many complex, real-world situations, problem solving and decision mak...

Algorithms for Causal Reasoning in Probability Trees

Probability trees are one of the simplest models of causal generative pr...

ACRE: Abstract Causal REasoning Beyond Covariation

Causal induction, i.e., identifying unobservable mechanisms that lead to...

To do or not to do: finding causal relations in smart homes

Research in Cognitive Science suggests that humans understand and repres...

Causal Inference of Script Knowledge

When does a sequence of events define an everyday scenario and how can t...