Relationship of Causal Representation Learning and Data Governance to Treatment Response Prediction in Oncology Cohorts

Abstract

The prediction of treatment responses in oncology is a critical step toward realizing precision medicine. However, observational data derived from real-world clinical cohorts are often confounded by treatment assignment biases, missing values, and heterogeneous data collection practices. This paper presents a comprehensive framework integrating causal representation learning with robust data governance to improve the explainability and reliability of treatment effect estimation in oncology cohorts. By establishing strict data governance protocols, including metadata standardization and privacy-preserving harmonization, we ensure high-fidelity inputs for downstream analytical tasks. Subsequently, a causal representation learning approach is introduced to disentangle latent confounders from the observed patient covariates, enabling the estimation of unbiased counterfactual outcomes. The dual approach of governing data quality and enforcing causal structures mitigates spurious correlations that frequently compromise traditional predictive models. We provide an extensive analysis of the theoretical underpinnings, methodological design, and empirical validation of the proposed system. Through rigorous experimentation on synthetic and semi-synthetic oncology datasets, the framework demonstrates superior performance in predicting individualized treatment effects while maintaining strict compliance with healthcare data regulations. The findings underscore the necessity of combining algorithmic innovation with systematic data management to achieve trustworthy artificial intelligence in clinical oncology.