Interpretable Personalization via Policy Learning with Linear Decision Boundaries

no code implementations • 17 Mar 2020 • Zhaonan Qu, Isabella Qian, Zhengyuan Zhou

Our findings suggest that our proposed policy learning framework using tools from causal inference and Bayesian optimization provides a promising practical approach to interpretable personalization across a wide range of applications.

Bayesian Optimization BIG-bench Machine Learning +2