arXiv subjects

Dario Compagno

Publications and source records attributed to Dario Compagno.

3 recordsLinked to original sources

arXiv2026

Teleological Inference in Structural Causal Models via Intentional Interventions

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.

cs.AI↗

arXiv2024

Identifying Intended Effects with Causal Models

The aim of this paper is to extend the framework of causal inference, in particular as it has been developed by Judea Pearl, in order to model actions and identify their intended effects, in the direction opened by Elisabeth Anscombe. We show how intentions can be inferred from a causal model and its implied correlations observable in data. The paper defines confounding effects as the reasons why teleological inference may fail and introduces interference as a way to control for them. The ''fundamental problem'' of teleological inference is presented, explaining why causal analysis needs an extension in order to take intentions into account.

stat.ME↗

arXiv2023

Final models: A finalistic interpretation of statistical correlation

This paper aims to extend the framework of causal modelling to teleological explanations. It conceives final models as second-order models produced by interventions on first-order causal models. It shows why such formalisation permits us to realise a finalistic interpretation of statistical correlation which is compatible with its usual causal interpretation. Initially, the paper identifies some conceptual conditions for statistical teleological analysis, specifically involving interventions. Then, it describes an explanation procedure for action and presents one simple example of identifiable final models. Finally, it compares these results with what could be obtained within a purely causalist framework.

stat.ME↗