Causal Reasoning

Causal reasoning refers to the use of knowedge about cause-effect relationships in the world to support plausible inferences about events. Example applications of automated causal reasoning systems include solving diagnostic problems, determining guilt/innocence in legal cases, and interpreting events in daily life. Some related terms include:

• abductive reasoning: given a set of findings (the evidence), construct a plausible explanation for those findings
• parsimonious covering theory: a formal model of abductive reasoning based on causal relationships
• Bayesian networks: causal associative networks with associated conditional probability tables that can be used, among other things, to support abductive reasoning