Objection-Based Causal Networks

TL;DR

This paper presents objection-based causal networks, a novel framework that uses logical objections instead of probabilities to model causal dependencies, offering potentially more intuitive reasoning about causality.

Contribution

The paper introduces objection-based causal networks, extending probabilistic causal networks with a logical objection framework that retains key properties while enhancing interpretability.

Findings

Objection-based causal networks maintain properties similar to probabilistic networks.

Objections are more intuitive than probabilities for representing causal dependencies.

The framework offers a new perspective on causal reasoning using logical sentences.

Abstract

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.