Collaborative Research: Theory of Causal Learning — NSF Award to University of Washington (WA, $125,000)
How can we interpret results from complex machine learning algorithms? How can we mitigate the risks associated with using such models for policy decisions? This project addresses fundamental challenges in deriving valid, reliable, and interpretable causal conclusions from complex data using modern machine learning too
| Award title | Collaborative Research: Theory of Causal Learning |
|---|---|
| Award ID | 2514233 |
| Awardee | University of Washington |
| City | SEATTLE |
| State | WA |
| Amount obligated | $125,000 |
| Principal investigator | Fang Han |
| Program | STATISTICS |
| Start date | 09/15/2025 |
| Abstract | How can we interpret results from complex machine learning algorithms? How can we mitigate the risks associated with using such models for policy decisions? This project addresses fundamental challenges in deriving valid, reliable, and interpretable causal conclusions from complex data using modern machine learning tools. As machine learning becomes increasingly integral to disciplines such as medicine, economics, education, and the social sciences, the demand for causal insight --- beyond predi |
| Source | NSF Awards |
$799/mo
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