  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
causal relationships in diffusion processes, subsuming various important
settings such as discrete-time Markov decision processes. Finally, in fairly
intricate experiments and through sheer learning, our framework reveals and
quantifies causal links, which otherwise seem inexplicable.
