Counterfactual Temporal Point Processes. (arXiv:2111.07603v2 [cs.LG] UPDATED)

Machine learning models based on temporal point processes are the state of
the art in a wide variety of applications involving discrete events in
continuous time. However, these models lack the ability to answer
counterfactual questions, which are increasingly relevant as these models are
being used to inform targeted interventions. In this work, our goal is to fill
this gap. To this end, we first develop a causal model of thinning for temporal
point processes that builds upon the Gumbel-Max …

arxiv processes temporal