Child Kidney Dis.  2024 Oct;28(3):93-98. 10.3339/ckd.24.018.

Integrating predictive modeling and causal inference for advancing medical science

• Oh TR

• KMID: 2560477
• DOI: http://doi.org/10.3339/ckd.24.018

Abstract

Artificial intelligence (AI) is revolutionizing healthcare by providing tools for disease prediction, diagnosis, and patient management. This review focuses on two key AI methodologies in healthcare: predictive modeling and causal inference. Predictive models excel in identifying patterns to forecast outcomes but are limited in explaining the underlying causes. In contrast, causal inference focuses on understanding cause-and-effect relationships, which makes effective medical interventions possible. Although randomized controlled trials (RCTs) are the gold standard for causal inference, they face limitations including cost and ethical concerns. As alternatives, emulated RCTs and advanced machine learning techniques have emerged for estimating causal effects, bridging the gap between prediction and causality. Additionally, Shapley values and Local Interpretable Model-Agnostic Explanations improve the interpretability of complex AI models, making them more actionable in clinical settings. Integrating prediction and causal inference holds great promise for advancing personalized medicine, enhancing patient outcomes, and optimizing healthcare delivery. However, careful application of AI tools is crucial to avoid misinterpretation and maximize their potential.

Keyword

Artificial intelligence; Causality; Forecasting

Figure

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  Fig. 1. Two paradigms of data analyses. This figure illustrates the two primary paradigms in data analytics: “prediction” and “causal inference.” An example of such “prediction” is the prediction of immunoglobulin A nephropathy progression in pediatric patients based on histological and demographic data, identifying patterns and correlations without addressing the underlying causes. In “causal inference,” randomized controlled trials are used to determine whether a specific treatment directly improves patient outcomes, focusing on cause-and-effect relationships.

Reference

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