"Cause"is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of"Causal Machine Learning"

2025-01-10

AI Summary

Mainstream causal machine learning paradigms reduce “causation” to statistical association or algorithmically decidable relations, thereby neglecting its irreducibly practical and context-sensitive nature—especially in complex natural and social sciences. Method: Drawing on ordinary-language philosophy, cross-disciplinary analysis of scientific practice, and epistemological reflection, the author advocates a mechanistic, multi-level (e.g., physiological–biochemical–behavioral), and multi-disciplinary evidential integration framework, augmented by hermeneutic sensibility and an open-systems perspective. Contribution/Results: The paper rigorously argues that causation is not an abstract ontological category but a context-dependent, non-reducible practical concept. It proposes an integrative causal research program grounded in this view, offering a novel philosophical foundation for interdisciplinary fields such as cognitive science and substantially expanding the methodological boundaries of causal discovery beyond purely statistical or algorithmic approaches.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Causal Learning has emerged as a major theme of AI in recent years, promising to use special techniques to reveal the true nature of cause and effect in a number of important domains. We consider the Epistemology of learning and recognizing true cause and effect phenomena. Through thought exercises on the customary use of the word ''cause'', especially in scientific domains, we investigate what, in practice, constitutes a valid causal claim. We recognize the word's uses across scientific domains in disparate form but consistent function within the scientific paradigm. We highlight fundamental distinctions of practice that can be performed in the natural and social sciences, highlight the importance of many systems of interest being open and irreducible and identify the important notion of Hermeneutic knowledge for social science inquiry. We posit that the distinct properties require that definitive causal claims can only come through an agglomeration of consistent evidence across multiple domains and levels of abstraction, such as empirical, physiological, biochemical, etc. We present Cognitive Science as an exemplary multi-disciplinary field providing omnipresent opportunity for such a Research Program, and highlight the main general modes of practice of scientific inquiry that can adequately merge, rather than place as incorrigibly conflictual, multi-domain multi-abstraction scientific practices and language games.

Problem

• Causal Learning
• Artificial Intelligence
• Complex Phenomena

Innovation

• Causal Learning
• Interdisciplinary Approach
• Unified Methodology