Causal Imitation Learning under Expert-Observable and Expert-Unobservable Confounding

Understand

We propose a general framework for causal Imitation Learning (IL) with hidden confounders, which subsumes several existing settings.

• Our framework accounts for two types of hidden confounders: (a) variables observed by the expert but not by the imitator, and (b) confounding noise hidden from both.
• By leveraging trajectory histories as instruments, we reformulate causal IL in our framework into a Conditional Moment Restriction (CMR) problem.
• We propose DML-IL, an algorithm that solves this CMR problem via instrumental variable regression, and upper bound its imitation gap.