Causal Machine Learning for Predicting Treatment Outcomes

MCML Authors

Stefan Feuerriegel

Prof. Dr.

Principal Investigator

Artificial Intelligence in Management

Dennis Frauen

→ Group Stefan Feuerriegel
 Artificial Intelligence in Management

Valentyn Melnychuk

→ Group Stefan Feuerriegel
 Artificial Intelligence in Management

Jonas Schweisthal

→ Group Stefan Feuerriegel
 Artificial Intelligence in Management

Konstantin Heß

→ Group Stefan Feuerriegel
 Artificial Intelligence in Management

Niki Kilbertus

Prof. Dr.

Principal Investigator

Ethics in Systems Design and Machine Learning

Abstract

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.

article

Nature Medicine

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Research Areas

 A1 | Statistical Foundations & Explainability

 A3 | Computational Models