  We present a simple reduction from sequential estimation to sequential
changepoint detection (SCD). In short, suppose we are interested in detecting
changepoints in some parameter or functional $\theta$ of the underlying
distribution. We demonstrate that if we can construct a confidence sequence
(CS) for $\theta$, then we can also successfully perform SCD for $\theta$. This
is accomplished by checking if two CSs -- one forwards and the other backwards
-- ever fail to intersect. Since the literature on CSs has been rapidly
evolving recently, the reduction provided in this paper immediately solves
several old and new change detection problems. Further, our "backward CS",
constructed by reversing time, is new and potentially of independent interest.
We provide strong nonasymptotic guarantees on the frequency of false alarms and
detection delay, and demonstrate numerical effectiveness on several problems.
