Graph Causal Optimal Transport
Abstract
Graph causal optimal transport is a recent generalisation of causal optimal transport in which the allowed couplings satisfy causal restrictions given by a directed graph. Inspired by applications to structural causal models, it was originally introduced in Eckstein and Cheridito (2023). We study fundamental properties of graph causal optimal transport, with a particular focus on its induced Wasserstein distance. Our main result is a full characterisation of the directed graphs for which this associated Wasserstein distance is indeed a metric, an open problem in the original paper. We fully characterise the gluing properties of graph causal couplings, prove denseness of Monge maps, and provide a dynamic programming principle. Finally, we present an application to continuity of stochastic team problems. Based on joint work with Jan Obloj.