Article
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Causal Economic Machine Learning (CEML)
Version 1
: Received: 16 August 2024 / Approved: 19 August 2024 / Online: 19 August 2024 (13:16:45 CEST)
How to cite: Horton, A. Causal Economic Machine Learning (CEML). Preprints 2024, 2024081344. https://doi.org/10.20944/preprints202408.1344.v1 Horton, A. Causal Economic Machine Learning (CEML). Preprints 2024, 2024081344. https://doi.org/10.20944/preprints202408.1344.v1
Abstract
This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML) built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral economics (BE) based on its central feature of causal coupling (CC), which models decisions as requiring upfront costs, some certain and some uncertain, in anticipation of future, uncertain causally-linked benefits. This multi-period, causal-linked process incorporating certainty and uncertainty replaces the single period lottery outcomes augmented with intertemporal discounting used in EUT and BE, providing a more realistic framework for AI machine learning modelling and real world application. It is mathematically demonstrated that EUT and BE are constrained versions of CE. Causal coupling can also be applied at the macroeconomic level to gauge the effectiveness of policies that deliver various levels of cost and benefit coupling for individuals. With the growing interest in natural experiments in statistics and CML across many fields, such as healthcare, economics and business, there is a large potential opportunity to run AI models on CE foundations and compare results to models based on traditional decision making models that focus only on rationality, bounded to various degrees.
Keywords
Artificial Intelligence, Machine Learning, Causal Machine Learning, Causal Economics, Causal Economic Machine Learning, Causal Inference, Natural Experiments, Expected Utility, Behavioral Economics
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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