A flexible approach to sequential prediction under intervention

2026-02-27

AI Summary

This work proposes a causally consistent predictive framework to address the challenges of double-counting intervention effects and evaluating multiple preventive interventions in sequential risk prediction. By integrating causal inference, mediation analysis, and predictive modeling, the framework employs an unexposed mediator model, a modifiable risk factor model, and a two-component structure to enable flexible evaluation of arbitrary intervention strategies under a known causal graph while avoiding double-counting of intervention effects. Empirical evaluation in the context of primary prevention of cardiovascular disease demonstrates that the proposed method yields more accurate and causally interpretable sequential risk predictions.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

We propose a causal predictive framework for estimating risk under preventative interventions. The Unexposed Mediator Model maintains mediators that are also predictors at their unexposed level, removing double counting of intervention effects at followup visits. The Modifiable Risk Factor Model handles multiple interventions flexibly by modelling their effects via mediators that are also predictors, assuming a known causal structure. The Two Component Model combines a predictive baseline model with an intervention model to improve predictive performance. We illustrate the framework in primary prevention of cardiovascular disease. The proposed models allow arbitrary interventions to be evaluated within a prediction under intervention framework, with causally consistent risk estimates across repeated visits. Limitations include reliance on predictor values from an arbitrary first visit, requirements for causal structural knowledge, and a consistency assumption, that interventions with identical effects on predictors have identical effects on outcomes, which warrant further investigation.

Problem

• sequential prediction
• intervention
• causal inference
• mediators
• risk estimation

Innovation

• causal prediction
• intervention modeling
• mediator adjustment
• sequential risk estimation
• modifiable risk factors