  We consider the problem of sequential change detection, where the goal is to
design a scheme for detecting any changes in a parameter or functional $\theta$
of the data stream distribution that has small detection delay, but guarantees
control on the frequency of false alarms in the absence of changes. In this
paper, we describe a simple reduction from sequential change detection to
sequential estimation using confidence sequences: we begin a new
$(1-\alpha)$-confidence sequence at each time step, and proclaim a change when
the intersection of all active confidence sequences becomes empty. We prove
that the average run length is at least $1/\alpha$, resulting in a change
detection scheme with minimal structural assumptions~(thus allowing for
possibly dependent observations, and nonparametric distribution classes), but
strong guarantees. Our approach bears an interesting parallel with the
reduction from change detection to sequential testing of Lorden (1971) and the
e-detector of Shin et al. (2022).
