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

• URL: http://arxiv.org/abs/2501.05844v2
• Date: Sun, 02 Feb 2025 10:56:42 GMT
• Title: "Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of "Causal Machine Learning"
• Authors: Vyacheslav Kungurtsev, Leonardo Christov Moore, Gustav Sir, Martin Krutsky,
• Abstract summary: Causal Learning has emerged as a major theme of research in statistics and machine learning. In this paper we consider recognizing true cause and effect phenomena.
• Score: 2.5782973781085383
• License:
• Abstract: Causal Learning has emerged as a major theme of research in statistics and machine learning in recent years, promising specific computational techniques to apply to datasets that reveal the true nature of cause and effect in a number of important domains. In this paper we consider the epistemology of recognizing true cause and effect phenomena. We apply the Ordinary Language method of engaging on the customary use of the word 'cause' to investigate valid semantics of reasoning about cause and effect. We recognize that the grammars of cause and effect are fundamentally distinct in form across scientific domains, yet they maintain a consistent and central function. This function can best be described as the mechanism underlying fundamental forces of influence as considered prominent in the respective scientific domain. We demarcate 1) physics and engineering as domains wherein mathematical models are sufficient to comprehensively describe causality, 2) biology as introducing challenges of emergence while providing opportunities for showing consistent mechanisms across scale, and 3) the social sciences as introducing grander difficulties for establishing models of low prediction error but providing, through Hermeneutics, the potential for findings that are still instrumentally useful to individuals. We posit that definitive causal claims regarding a given phenomenon (writ large) can only come through an agglomeration of consistent evidence across multiple domains. This presents important methodological questions as far as harmonizing between language games and emergence across scales. Given the role of epistemic hubris in the contemporary crisis of credibility in the sciences, exercising greater caution as far as communicating precision as to the real degree of certainty certain evidence provides for rich collections of open problems in optimizing integration of different findings.

Related papers

• Causality can systematically address the monsters under the bench(marks) [64.36592889550431]
  Benchmarks are plagued by various biases, artifacts, or leakage. Models may behave unreliably due to poorly explored failure modes. causality offers an ideal framework to systematically address these challenges.
  arXiv  Detail & Related papers  (2025-02-07T17:01:37Z)
• Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation [59.138470433237615]
  We introduce statistical metrics that quantify both the linguistic and visual skew of a dataset for relational learning. We show that systematically controlled metrics are strongly predictive of generalization performance. This work informs an important direction towards quality-enhancing the data diversity or balance to scaling up the absolute size.
  arXiv  Detail & Related papers  (2024-03-25T03:18:39Z)
• Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies [12.78006421209864]
  Causal emergence theory employs measures of causality to quantify emergence. Two key problems are addressed: quantifying causal emergence and identifying it in data. We highlighted that the architectures used for identifying causal emergence are shared by causal representation learning, causal model abstraction, and world model-based reinforcement learning.
  arXiv  Detail & Related papers  (2023-12-28T04:20:46Z)
• A Causal Framework for Decomposing Spurious Variations [68.12191782657437]
  We develop tools for decomposing spurious variations in Markovian and Semi-Markovian models. We prove the first results that allow a non-parametric decomposition of spurious effects. The described approach has several applications, ranging from explainable and fair AI to questions in epidemiology and medicine.
  arXiv  Detail & Related papers  (2023-06-08T09:40:28Z)
• Discovering Causal Relations and Equations from Data [23.802778299505288]
  This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of Physics. We provide a taxonomy for observational causal and equation discovery, point out connections, and showcase a complete set of case studies. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.
  arXiv  Detail & Related papers  (2023-05-21T19:22:50Z)
• Causal Deep Learning [77.49632479298745]
  Causality has the potential to transform the way we solve real-world problems. But causality often requires crucial assumptions which cannot be tested in practice. We propose a new way of thinking about causality -- we call this causal deep learning.
  arXiv  Detail & Related papers  (2023-03-03T19:19:18Z)
• Causality, Causal Discovery, and Causal Inference in Structural Engineering [1.827510863075184]
  This paper builds a case for causal discovery and causal inference from a civil and structural engineering perspective. More specifically, this paper outlines the key principles of causality and the most commonly used algorithms and packages for causal discovery and causal inference.
  arXiv  Detail & Related papers  (2022-04-04T14:49:47Z)
• The Causal Neural Connection: Expressiveness, Learnability, and Inference [125.57815987218756]
  An object called structural causal model (SCM) represents a collection of mechanisms and sources of random variation of the system under investigation. In this paper, we show that the causal hierarchy theorem (Thm. 1, Bareinboim et al., 2020) still holds for neural models. We introduce a special type of SCM called a neural causal model (NCM), and formalize a new type of inductive bias to encode structural constraints necessary for performing causal inferences.
  arXiv  Detail & Related papers  (2021-07-02T01:55:18Z)
• Towards Causal Representation Learning [96.110881654479]
  The two fields of machine learning and graphical causality arose and developed separately. There is now cross-pollination and increasing interest in both fields to benefit from the advances of the other.
  arXiv  Detail & Related papers  (2021-02-22T15:26:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.