Teleological Inference in Structural Causal Models via Intentional Interventions

2026-03-19

AI Summary

This study addresses the challenge of modeling and inferring goal-directed behavior of agents within the framework of structural causal models (SCMs). To this end, the authors propose a novel "intentional intervention operator" and develop a Structural Finality Model (SFM), which interprets observational data as the outcome of purposeful interventions enacted by an agent to achieve specific goals. By leveraging counterfactual reasoning, the model enables identification of the agent’s latent intentions. This work is the first to introduce a time-agnostic mechanism for intentional interventions into the SCM framework, thereby establishing identifiability and enabling empirical detection of goal-directed behavior. The approach significantly extends the theoretical boundaries and practical applicability of causal models in teleological reasoning.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Structural causal models (SCMs) were conceived to formulate and answer causal questions. This paper shows that SCMs can also be used to formulate and answer teleological questions, concerning the intentions of a state-aware, goal-directed agent intervening in a causal system. We review limitations of previous approaches to modeling such agents, and then introduce intentional interventions, a new time-agnostic operator that induces a twin SCM we call a structural final model (SFM). SFMs treat observed values as the outcome of intentional interventions and relate them to the counterfactual conditions of those interventions (what would have happened had the agent not intervened). We show how SFMs can be used to empirically detect agents and to discover their intentions.

Problem

• teleological inference
• structural causal models
• intentional interventions
• goal-directed agents
• causal reasoning

Innovation

• intentional interventions
• structural final model
• teleological inference
• structural causal models
• counterfactual reasoning