Estimating and Evaluating Counterfactual Prediction Models.

Area of Science:

• * Statistical modeling and machine learning in healthcare.
• * Causal inference and predictive analytics.

Background:

• * Counterfactual prediction models are essential when deploying models in new environments or for decision-making under hypothetical scenarios.
• * Estimating and evaluating these models is complex due to the lack of observed outcomes for all treatment strategies.
• * Traditional (factual) prediction differs significantly from counterfactual prediction in data requirements.

Purpose of the Study:

• * To outline methods for estimating counterfactual prediction models.
• * To detail approaches for assessing the performance of counterfactual prediction models.
• * To describe strategies for model selection and tuning parameter optimization.

Main Methods:

• * Development of identification and estimation results for counterfactual prediction models.
• * Inclusion of multiple performance measures: loss-based metrics, Area Under the Receiver Operating Characteristic Curve (AUC), and calibration curves.
• * Ensuring valid performance estimates even with potentially misspecified predictive models.

Main Results:

• * Established methods for estimating counterfactual prediction models and their performance.
• * Demonstrated the validity of performance estimates under counterfactual intervention, irrespective of model specification.
• * Successful application of methods to develop a cardiovascular disease risk prediction model.

Conclusions:

• * The proposed methods facilitate robust estimation and evaluation of counterfactual prediction models.
• * The approach allows for wider applicability of counterfactual prediction, even with imperfect models.
• * The study provides a practical framework for developing predictive models in complex, evolving healthcare settings.