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[2602.23303] Inferential Mechanics Part 1: Causal Mechanistic Theories of Machine Learning in Chemical Biology with Implications
Summary
This article introduces 'Inferential Mechanics,' a framework combining causal theories with machine learning in chemical biology, addressing the limitations of current predictive models.
Why It Matters
The paper highlights the need for a unified theoretical approach to integrate causality into machine learning applications in the natural sciences. This is crucial for improving the reliability of predictions and understanding underlying mechanisms in chemical biology, which can lead to more effective research outcomes.
Key Takeaways
- Introduces a new framework for integrating causality into machine learning models in chemical biology.
- Emphasizes the importance of understanding causal structures to improve predictive accuracy.
- Presents initial proof of concepts using Akt inhibitors to demonstrate the framework's applicability.
Computer Science > Machine Learning arXiv:2602.23303 (cs) [Submitted on 26 Feb 2026] Title:Inferential Mechanics Part 1: Causal Mechanistic Theories of Machine Learning in Chemical Biology with Implications Authors:Ilya Balabin, Thomas M. Kaiser View a PDF of the paper titled Inferential Mechanics Part 1: Causal Mechanistic Theories of Machine Learning in Chemical Biology with Implications, by Ilya Balabin and Thomas M. Kaiser View PDF Abstract:Machine learning techniques are now routinely encountered in research laboratories across the globe. Impressive progress has been made through ML and AI techniques with regards to large data set processing. This progress has increased the ability of the experimenter to digest data and make novel predictions regarding phenomena of interest. However, machine learning predictors generated from data sets taken from the natural sciences are often treated as black boxes which are used broadly and generally without detailed consideration of the causal structure of the data set of interest. Work has been attempted to bring causality into discussions of machine learning models of natural phenomena; however, a firm and unified theoretical treatment is lacking. This series of three papers explores the union of chemical theory, biological theory, probability theory and causality that will correct current causal flaws of machine learning in the natural sciences. This paper, Part 1 of the series, provides the formal framework of the foundational ...
