Counterfactual Causality from First Principles?

TL;DR

This paper critically examines current approaches to counterfactual causality, highlighting key shortcomings and proposing future research directions to develop more rigorous, dynamic, and abstract-aware causality frameworks.

Contribution

It identifies three main issues in existing causality methods and sketches potential lines of work to address these challenges from a computer science perspective.

Findings

Current causality definitions lack precise requirement-driven frameworks.

Existing models do not adequately support system dynamics.

Causality analysis behavior in abstraction is poorly understood.

Abstract

In this position paper we discuss three main shortcomings of existing approaches to counterfactual causality from the computer science perspective, and sketch lines of work to try and overcome these issues: (1) causality definitions should be driven by a set of precisely specified requirements rather than specific examples; (2) causality frameworks should support system dynamics; (3) causality analysis should have a well-understood behavior in presence of abstraction.