From algorithms to action: improving patient care requires causality
This tackles the issue of harmful medical decisions from non-causal models for clinicians and patients, representing a foundational shift rather than incremental improvement.
The paper addresses the problem that accurate cancer outcome prediction models can cause harm in treatment decisions due to ignoring causality, and proposes methods to build and validate models that are useful for decision-making.
In cancer research there is much interest in building and validating outcome predicting outcomes to support treatment decisions. However, because most outcome prediction models are developed and validated without regard to the causal aspects of treatment decision making, many published outcome prediction models may cause harm when used for decision making, despite being found accurate in validation studies. Guidelines on prediction model validation and the checklist for risk model endorsement by the American Joint Committee on Cancer do not protect against prediction models that are accurate during development and validation but harmful when used for decision making. We explain why this is the case and how to build and validate models that are useful for decision making.