  Human decision making is well known to be imperfect and the ability to
analyse such processes individually is crucial when attempting to aid or
improve a decision-maker's ability to perform a task, e.g. to alert them to
potential biases or oversights on their part. To do so, it is necessary to
develop interpretable representations of how agents make decisions and how this
process changes over time as the agent learns online in reaction to the accrued
experience. To then understand the decision-making processes underlying a set
of observed trajectories, we cast the policy inference problem as the inverse
to this online learning problem. By interpreting actions within a potential
outcomes framework, we introduce a meaningful mapping based on agents choosing
an action they believe to have the greatest treatment effect. We introduce a
practical algorithm for retrospectively estimating such perceived effects,
alongside the process through which agents update them, using a novel
architecture built upon an expressive family of deep state-space models.
Through application to the analysis of UNOS organ donation acceptance
decisions, we demonstrate that our approach can bring valuable insights into
the factors that govern decision processes and how they change over time.
