  We propose a dynamic allocation procedure that increases power and efficiency
when measuring an average treatment effect in sequential randomized trials
exploiting some subjects' previous assessed responses. Subjects arrive
sequentially and are either randomized or paired to a previously randomized
subject and administered the alternate treatment. The pairing is made via a
dynamic matching criterion that iteratively learns which specific covariates
are important to the response. We develop estimators for the average treatment
effect as well as an exact test. We illustrate our method's increase in
efficiency and power over other allocation procedures in both simulated
scenarios and a clinical trial dataset. An R package "SeqExpMatch" for use by
practitioners is available.
