Section: Research Program

Causal modeling

The team is working on a new class of models for modeling physical systems, starting from measured data and accounting for their dynamics [40]. The idea is to statistically describe the evolution of a system in terms of causally-equivalent states; states that lead to the same predictions [33]. Transitions between these states can be reconstructed from data, leading to a theoretically-optimal predictive model [63]. In practice, however, no algorithm is currently able to reconstruct these models from data in a reasonable time and without substantial discrete approximations. Recent progress now allows a continuous formulation of predictive causal models. Within this framework, more efficient algorithms may be found. The broadened class of predictive models promises a new perspective on structural complexity in many applications.