Final models: A finalistic interpretation of statistical correlation

TL;DR

This paper extends causal modeling to include teleological explanations by formalizing final models as second-order models, enabling a finalistic interpretation of statistical correlation compatible with causal views.

Contribution

It introduces a formal framework for final models as second-order interventions, bridging causal and teleological interpretations of statistical correlation.

Findings

Final models can be formalized as second-order models.

The framework allows a finalistic interpretation compatible with causal analysis.

An example of identifiable final models is provided.

Abstract

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.