[Paper Review] Structure-Based Causes and Explanations in the Independent Choice Logic

Alberto Finzi, Thomas Lukasiewicz|arXiv (Cornell University)|Oct 19, 2012

Bayesian Modeling and Causal Inference35 references22 citations

TL;DR

This paper integrates Pearl's structural-model approach to causality with Poole's Independent Choice Logic (ICL), enabling probabilistic causal reasoning in first-order, action-based domains. By mapping ICL to structural causal models, the framework supports formal definitions of actual cause, explanation, and partial explanation, enhancing ICL with causal semantics while extending structural models with first-order and action modeling capabilities.

ABSTRACT

This paper is directed towards combining Pearl's structural-model approach to causal reasoning with high-level formalisms for reasoning about actions. More precisely, we present a combination of Pearl's structural-model approach with Poole's independent choice logic. We show how probabilistic theories in the independent choice logic can be mapped to probabilistic causal models. This mapping provides the independent choice logic with appealing concepts of causality and explanation from the structural-model approach. We illustrate this along Halpern and Pearl's sophisticated notions of actual cause, explanation, and partial explanation. This mapping also adds first-order modeling capabilities and explicit actions to the structural-model approach.

Motivation & Objective

• To bridge Pearl's structural-model approach to causality with high-level formalisms like the Independent Choice Logic (ICL).
• To enable formal causal reasoning—such as actual cause and explanation—within ICL's probabilistic framework.
• To extend the structural-model approach with first-order logic and explicit action representation.
• To provide a unified framework for causal explanation in complex, probabilistic, action-based domains.

Proposed method

• Mapping ICL theories to structural causal models by defining structural equations that capture probabilistic dependencies.
• Using do-calculus and interventions to formalize causal effects within the ICL framework.
• Defining actual cause using Halpern and Pearl's structural equation model (SEM) criteria within the ICL context.
• Introducing partial explanation via structural model decomposition and intervention analysis.
• Encoding actions explicitly in structural equations to model their causal impact.
• Leveraging ICL's first-order logic capabilities to represent causal structures and dependencies at scale.

Experimental results

Research questions

• RQ1 How can Pearl’s structural-model approach to causality be formally integrated with the Independent Choice Logic?
• RQ2 What constitutes an actual cause in a probabilistic, first-order, action-based domain under the ICL framework?
• RQ3 How can explanations and partial explanations be systematically derived in ICL using structural causal models?
• RQ4 Can the structural-model approach be extended to support first-order modeling and explicit actions?
• RQ5 What are the formal semantics for causal explanation in ICL when combined with do-calculus and intervention logic?

Key findings

• The mapping of ICL to structural causal models enables formal definitions of actual cause, explanation, and partial explanation using Halpern and Pearl’s criteria.
• The integration allows ICL to reason about interventions and counterfactuals through do-calculus, enhancing its explanatory power.
• First-order modeling capabilities in ICL are successfully extended to the structural-model approach, enabling scalable causal reasoning.
• Explicit actions in ICL are modeled as interventions in structural equations, allowing causal impact analysis.
• The framework supports partial explanations by decomposing structural models and analyzing intervention effects on subcomponents.
• The approach provides a principled, formal semantics for causal reasoning in complex, probabilistic domains with actions.