Objection-Based Causal Networks
This work addresses the need for more intuitive causal modeling tools for researchers and practitioners in fields like AI and statistics, though it appears incremental as it builds directly on probabilistic causal networks.
The paper tackles the problem of making causal networks more intuitive by introducing objection-based causal networks, which replace probabilities with logical objections to denote conditions where causal dependencies do not exist, resulting in networks that retain most properties of probabilistic causal networks while being arguably more intuitive.
This paper introduces the notion of objection-based causal networks which resemble probabilistic causal networks except that they are quantified using objections. An objection is a logical sentence and denotes a condition under which a, causal dependency does not exist. Objection-based causal networks enjoy almost all the properties that make probabilistic causal networks popular, with the added advantage that objections are, arguably more intuitive than probabilities.