Collaborative Research: Causal Discovery and Individualized Policy Optimization for Human — NSF Award to University of California-
Recent advancements in natural language processing (NLP) have led to a rapid increase in available text data, sparking research developments in precision medicine, economics, recommendation systems, and social science. While existing deep learning methods can predict outcomes accurately, it remains unclear how to disen
| Award title | Collaborative Research: Causal Discovery and Individualized Policy Optimization for Human |
|---|---|
| Award ID | 2401271 |
| Awardee | University of California-Irvine |
| City | IRVINE |
| State | CA |
| Amount obligated | $300,000 |
| Principal investigator | Hengrui Cai |
| Program | STATISTICS, CDS&E-MSS |
| Start date | 09/01/2024 |
| Abstract | Recent advancements in natural language processing (NLP) have led to a rapid increase in available text data, sparking research developments in precision medicine, economics, recommendation systems, and social science. While existing deep learning methods can predict outcomes accurately, it remains unclear how to disentangle, quantify, and use complex relationships among observed textual variables. Causal inference presents a solution for extracting trustworthy causal relationships and establish |
| Source | NSF Awards |
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