  Imitation learning suffers from causal confusion. This phenomenon occurs when
learned policies attend to features that do not causally influence the expert
actions but are instead spuriously correlated. Causally confused agents produce
low open-loop supervised loss but poor closed-loop performance upon deployment.
We consider the problem of masking observed confounders in a disentangled
representation of the observation space. Our novel masking algorithm leverages
the usual ability to intervene in the initial system state, avoiding any
requirement involving expert querying, expert reward functions, or causal graph
specification. Under certain assumptions, we theoretically prove that this
algorithm is conservative in the sense that it does not incorrectly mask
observations that causally influence the expert; furthermore, intervening on
the initial state serves to strictly reduce excess conservatism. The masking
algorithm is applied to behavior cloning for two illustrative control systems:
CartPole and Reacher.
