Training AI models to answer 'what if?' questions could improve medical treatments

Causal machine learning (ML) and classical machine learning differ in their approach to handling therapy decisions, with causal ML having the ability to estimate treatment outcomes better than classical ML.

Classical machine learning focuses on recognizing patterns and discovering correlations, but it does not address the principle of cause and effect. When making therapy decisions for a patient, the 'why' is crucial for achieving the best outcomes. However, classical ML cannot effectively answer 'why?' and 'what if?' questions.

On the other hand, causal machine learning can estimate treatment outcomes and make it easier for clinicians to personalize treatment strategies, improving patients' health individually. Causal ML can answer 'what if?' questions and gauge the effect of a cause, allowing it to determine the best treatment plan for a specific patient.

For example, in the case of someone at risk of developing diabetes, classical ML would aim to predict the probability of a patient developing the disease based on their risk factors. In contrast, causal ML would be able to estimate how the risk changes if the patient receives an anti-diabetes drug and determine whether metformin or another treatment plan would be best.

In summary, causal ML offers a more complex and personalized approach to therapy decisions compared to classical ML by addressing causal relationships and providing answers to 'what if?' questions.

Causal machine learning (ML) improves the personalization of treatment strategies for patients by enabling clinicians to estimate treatment outcomes and make more informed decisions. Unlike classical ML, which recognizes patterns and discovers correlations, causal ML can address the question of cause and effect, allowing it to answer "what if?" questions. This helps clinicians understand how a patient's risk factors may change with a specific treatment and estimate the effectiveness of different treatment options, such as choosing between medications like metformin or an alternative treatment plan for a patient at risk of developing diabetes. By providing this deeper insight, causal ML supports clinicians in personalizing treatment strategies to improve individual patient health.