Summary
Data-driven personalized decision-making has become increasingly important across many fields, such as health sciences where tailoring treatments to individual patients can improve effectiveness and reduce adverse effects. Achieving reliable personalized decisions requires understanding cause-and-effect relationships between actions and outcomes. However, most real-world data sources, such as electronic medical records, health surveys, and social media data, are observational rather than randomized, making causal relationships difficult to establish. In these settings, hidden or unmeasured fac
