  Ad hoc teamwork requires an agent to cooperate with unknown teammates without
prior coordination. Many works propose to abstract teammate instances into
high-level representation of types and then pre-train the best response for
each type. However, most of them do not consider the distribution of teammate
instances within a type. This could expose the agent to the hidden risk of
\emph{type confounding}. In the worst case, the best response for an abstract
teammate type could be the worst response for all specific instances of that
type. This work addresses the issue from the lens of causal inference. We first
theoretically demonstrate that this phenomenon is due to the spurious
correlation brought by uncontrolled teammate distribution. Then, we propose our
solution, CTCAT, which disentangles such correlation through an instance-wise
teammate feedback rectification. This operation reweights the interaction of
teammate instances within a shared type to reduce the influence of type
confounding. The effect of CTCAT is evaluated in multiple domains, including
classic ad hoc teamwork tasks and real-world scenarios. Results show that CTCAT
is robust to the influence of type confounding, a practical issue that directly
hazards the robustness of our trained agents but was unnoticed in previous
works.
