Compact Representations of Extended Causal Models

TL;DR

This paper introduces a method to create compact representations of extended causal models that incorporate causal structure and normality considerations, simplifying complex models for better analysis.

Contribution

It presents a novel approach to represent extended causal models more efficiently, reducing their complexity while retaining essential causal and normality information.

Findings

Compact representations are achievable for complex extended causal models

The approach simplifies analysis of causation with normality considerations

Potential for improved computational efficiency in causal reasoning

Abstract

Judea Pearl was the first to propose a definition of actual causation using causal models. A number of authors have suggested that an adequate account of actual causation must appeal not only to causal structure, but also to considerations of normality. In earlier work, we provided a definition of actual causation using extended causal models, which include information about both causal structure and normality. Extended causal models are potentially very complex. In this paper, we show how it is possible to achieve a compact representation of extended causal models.