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

Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies