  Medical diagnosis assistant (MDA) aims to build an interactive diagnostic
agent to sequentially inquire about symptoms for discriminating diseases.
However, since the dialogue records used to build a patient simulator are
collected passively, the data might be deteriorated by some task-unrelated
biases, such as the preference of the collectors. These biases might hinder the
diagnostic agent to capture transportable knowledge from the simulator. This
work attempts to address these critical issues in MDA by taking advantage of
the causal diagram to identify and resolve two representative non-causal
biases, i.e., (i) default-answer bias and (ii) distributional inquiry bias.
Specifically, Bias (i) originates from the patient simulator which tries to
answer the unrecorded inquiries with some biased default answers. Consequently,
the diagnostic agents cannot fully demonstrate their advantages due to the
biased answers. To eliminate this bias and inspired by the propensity score
matching technique with causal diagram, we propose a propensity-based patient
simulator to effectively answer unrecorded inquiry by drawing knowledge from
the other records; Bias (ii) inherently comes along with the passively
collected data, and is one of the key obstacles for training the agent towards
"learning how" rather than "remembering what". For example, within the
distribution of training data, if a symptom is highly coupled with a certain
disease, the agent might learn to only inquire about that symptom to
discriminate that disease, thus might not generalize to the out-of-distribution
cases. To this end, we propose a progressive assurance agent, which includes
the dual processes accounting for symptom inquiry and disease diagnosis
respectively. The inquiry process is driven by the diagnosis process in a
top-down manner to inquire about symptoms for enhancing diagnostic confidence.
