Evaluating and testing for actionable treatment effect heterogeneity

Area of Science:

• Statistics
• Machine Learning
• Causal Inference

Background:

• Estimating heterogeneous treatment effects (HTEs) is crucial for personalized medicine and policy.
• A key challenge is assessing if HTE models offer a predictive advantage over non-heterogeneous models.

Purpose of the Study:

• To propose a procedure for evaluating the predictive advantage gained by incorporating HTE into prediction models.
• To quantify the improvement in prediction performance when using flexible HTE models versus constrained partial linear models.

Main Methods:

• Developed a procedure to compare flexible HTE models against partial linear models that lack heterogeneity.
• Utilized nested cross-validation techniques for robust prediction error inference.
• Generated confidence intervals for the gain in predictive performance.

Main Results:

• Introduced the 'predictive HTE p-value' to measure confidence in HTE model advantage.
• The procedure provides a direct assessment of the benefit of modeling HTE.
• The method is applicable to any technique incorporating treatment effect heterogeneity.

Conclusions:

• The proposed procedure reliably assesses the added value of HTE models in prediction.
• It enables researchers to quantify the predictive gains from accounting for individual treatment effect variations.
• This facilitates informed decisions on employing complex HTE models in practice.