A Dynamical View of the Question of Why

arXiv:2402.10240v1 Announce Type: new
Abstract: We address causal reasoning in multivariate time series data generated by stochastic processes. Existing approaches are largely restricted to static settings, ignoring the continuity and emission of variations across time. In contrast, we propose a learning paradigm that directly establishes causation between events in the course of time. We present two key lemmas to compute causal contributions and frame them as reinforcement learning problems. Our approach offers formal and computational tools for uncovering and quantifying …

abstract arxiv causation continuity contrast course cs.ai cs.lg cs.sy data eess.sy events generated multivariate paradigm processes question reasoning series stochastic time series type view