From Prediction to Prescription: Machine Learning and Causal Inference for the Heterogeneous Treatment Effect.

Area of Science:

• Computational epidemiology
• Biostatistics
• Machine learning in healthcare

Background:

• The growing volume and complexity of electronic health record data necessitate advanced analytical methods.
• Data-driven medical decision-making holds promise for personalized interventions.
• Extracting causal insights from observational health data is a significant challenge.

Purpose of the Study:

• To review the application of machine learning (ML) for estimating causal effects in individualized medical interventions.
• To bridge the gap between ML methodologies and epidemiological principles for causal inference.
• To guide the development of ML-based causal estimators using complex health data.

Main Methods:

• Review of ML techniques applied to causal inference problems.
• Discussion of study designs crucial for establishing causality.
• Explanation of methods for adjusting confounding bias using variable selection.
• Presentation of statistically precise formulations connecting ML to epidemiology.

Main Results:

• ML models can be adapted to support causal claims from complex health data.
• Specific methods for building causal estimators using ML are detailed.
• The importance of study design in causal inference is emphasized.
• Bridging ML and epidemiology facilitates robust causal effect estimation.

Conclusions:

• Machine learning offers powerful tools for data-driven individualized medical decision-making.
• Careful consideration of study design and causal inference methods is essential.
• This work provides a framework for leveraging ML in epidemiology for causal effect estimation.