Causal machine learning for predicting treatment outcomes

Causal machine learning (ML) provides flexible, data-driven methods to predict how well treatments will work and if they might have harmful side effects. This helps researchers and doctors evaluate drugs for safety and effectiveness.

One of the main advantages of causal ML is that it can estimate how each treatment will affect individual patients. This means doctors can make treatment decisions tailored to each patient’s needs.

Causal ML can use data from clinical trials as well as real-world sources like medical records. However, we need to be careful to avoid making biased or incorrect predictions.

In the article, they explain why causal ML is better than traditional statistical or ML methods, and they describe how it works. Finally, they give some advice on how to use causal ML reliably and how to apply it in real-world medical settings.

From the article:

Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic.

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