  Causal reversibility blends reversibility and causality for concurrent
systems. It indicates that an action can be undone provided that all of its
consequences have been undone already, thus making it possible to bring the
system back to a past consistent state. Time reversibility is instead
considered in the field of stochastic processes, mostly for efficient analysis
purposes. A performance model based on a continuous-time Markov chain is time
reversible if its stochastic behavior remains the same when the direction of
time is reversed. We bridge these two theories of reversibility by showing the
conditions under which causal reversibility and time reversibility are both
ensured by construction. This is done in the setting of a stochastic process
calculus, which is then equipped with a variant of stochastic bisimilarity
accounting for both forward and backward directions.
