Probabilistic Causal Reasoning

TL;DR

This paper introduces a probabilistic causal reasoning framework for predictive inference under uncertainty, focusing on persistence over time, with a polynomial-time decision procedure and a prototype implementation in manufacturing planning.

Contribution

It develops a novel theory combining probability and temporal reasoning to handle persistence, overcoming limitations of nonmonotonic temporal reasoning schemes.

Findings

Polynomial-time algorithm for probability of consequences

Effective integration of probability with temporal projection

Prototype system applied to manufacturing planning

Abstract

Predicting the future is an important component of decision making. In most situations, however, there is not enough information to make accurate predictions. In this paper, we develop a theory of causal reasoning for predictive inference under uncertainty. We emphasize a common type of prediction that involves reasoning about persistence: whether or not a proposition once made true remains true at some later time. We provide a decision procedure with a polynomial-time algorithm for determining the probability of the possible consequences of a set events and initial conditions. The integration of simple probability theory with temporal projection enables us to circumvent problems that nonmonotonic temporal reasoning schemes have in dealing with persistence. The ideas in this paper have been implemented in a prototype system that refines a database of causal rules in the course of applying those rules to construct and carry out plans in a manufacturing domain.