  RCTs sometimes test interventions that aim to improve existing services
targeted to a subset of individuals identified after randomization.
Accordingly, the treatment could affect the composition of service recipients
and the offered services. With such bias, intention-to-treat estimates using
data on service recipients and nonrecipients may be difficult to interpret.
This article develops causal estimands and inverse probability weighting (IPW)
estimators for complier populations in these settings, using a generalized
estimating equation approach that adjusts the standard errors for estimation
error in the IPW weights. While our focus is on more general clustered RCTs,
the methods also apply (reduce) to non-clustered RCTs. Simulations show that
the estimators achieve nominal confidence interval coverage under the assumed
identification conditions. An empirical application demonstrates the methods
using data from a large-scale RCT testing the effects of early childhood
services on children's cognitive development scores.
