Collaborative Research: New Bayesian Causal Methods for Personalized Decision-Making Under — NSF Award to Trustees of Boston Unive
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 b
| Award title | Collaborative Research: New Bayesian Causal Methods for Personalized Decision-Making Under |
|---|---|
| Award ID | 2610269 |
| Awardee | Trustees of Boston University |
| City | BOSTON |
| State | MA |
| Amount obligated | $73,241 |
| Principal investigator | Wei Jin |
| Program | STATISTICS |
| Start date | 06/01/2026 |
| Abstract | 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 randomi |
| Source | NSF Awards |
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