Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks

Understand

Causal inference explores the causation between actions and the consequent rewards on a covariate set.

• Recently deep learning has achieved a remarkable performance in causal inference, but existing statistical theories cannot well explain such an empirical success, especially when the covariates are high-dimensional.
• Most theoretical results in causal inference are asymptotic, suffer from the curse of dimensionality, and only work for the finite-action scenario.
• To bridge such a gap between theory and practice, this paper studies doubly robust off-policy learning by deep neural networks.