Title:      The Essential Role of Pair Matching in
Cluster-Randomized Experiments, with Application to the Mexican
Universal Health Insurance Evaluation

Authors:    Kosuke Imai, Gary King, Clayton Nall

Entrydate:  2007-07-17 08:14:25

Keywords:   causal inference, community intervention trials,
field experiments, group-randomized trials, health policy,
matched-pair design, noncompliance

Abstract:   A basic feature of many field experiments is that
investigators are only able to randomize clusters of individuals
-- such as households, communities, firms, medical practices,
schools, or classrooms -- even when the individual is the unit
of interest.  To recoup some of the resulting efficiency loss,
many studies pair similar clusters and randomize treatment
within pairs.  Other studies (including almost all published
political science field experiments) avoid pairing, in part
because some prominent methodological articles claim to have
identified serious problems with this 'matched-pair
cluster-randomized' design.  We prove that all such claims about
problems with this design are unfounded.  We then show that the
estimator for matched-pair designs favored in the literature is
appropriate only in situations where matching is not needed.  To
address this problem without modeling assumptions, we generalize
Neyman's (1923) approach and propose a simple new estimator with
much improved statistical properties.  We also introduce methods
to cope with individual-level noncompliance, which most existing
approaches incorrectly assume away.  We show that from the
perspective of, among other things, bias, efficiency, or power,
pairing should be used in cluster-randomized experiments
whenever feasible; failing to do so is equivalent to discarding
a considerable fraction of one's data.  We develop these
techniques in the context of a randomized evaluation we are
conducting of the Mexican Universal Health Insurance Program.

http://polmeth.wustl.edu/retrieve.php?id=706