compliance.score: Estimate compliance scores using covariates.
In kylepeyton/icsw: Inverse Compliance Score Weighting

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Estimate probability that units are compliers using generalized linear models for probability of being a complier or always-taker and for being an always-taker conditional on being a complier or always-taker. These compliance scores can be used in inverse probability weighting to estimate average treatment effects. In the case of one-sided non-compliance, this estimation is considerably simpler.

compliance.score(D, Z, W, weights = NULL,
  link = qnorm, inv.link = pnorm, genoud = TRUE,
  num.iter = ifelse(genoud, 200, 10000),
  one.sided = FALSE)

`D`	Binary treatment of interest.
`Z`	Binary instrument.
`W`	Matrix of covariates for compliance model.
`weights`	Observation weights.
`link`	Link function applied for linear models. Defaults to probit link function.
`inv.link`	Inverse link function (i.e., mean function) applied for linear models. Defaults to probit mean function.
`genoud`	Whether to use global optimization via genetic optimization from package `rgenoud`. Default, and highly recommended because the likelihood need not be concave. Otherwise, use the BOBYQA algorithm for constrained optimization from package `minqa`.
`num.iter`	Number of iterations of optimization routine.
`one.sided`	Whether non-compliance is one-sided (logical). When compliance is one-sided, the previous four arguments are ignored, and the compliance scores are estimated with probit regression.

A unit i is a complier if D_{i1} > D_{i0}, where D_{i1} and D_{i0} are the potential treatments for unit i when Z is set to 1 and 0. This is a latent (unobserved) characteristic of individual units, since each unit is only observed with one value of Z.

By default this function uses genetic optimization via genoud because the loss function for the complier scores is not necessarily convex.

Vector of estimated probabilities of being a complier (i.e., compliance scores).

Requires rgenoud package if genoud = TRUE. Requires minqa package if genoud = FALSE.

Peter M. Aronow <peter.aronow@yale.edu>, Dean Eckles <icsw@deaneckles.com>, Kyle Peyton <kyle.peyton@yale.edu>

Peter M. Aronow and Allison Carnegie. (2013). Beyond LATE: Estimation of the average treatment effect with an instrumental variable. Political Analysis.

Used by icsw.tsls.

# Load example dataset, see help(FoxDebate) for details
data(FoxDebate)

# Matrix of covariates
covmat <- with(FoxDebate, cbind(partyid, pnintst, watchnat, educad, readnews, 
                                gender, income, white))

# Estimate compliance scores with covariates, assuming (default) 
#   case of two-sided non-compliance
cscoreout <- with(FoxDebate, compliance.score(D = watchpro, Z = conditn,
                                              W = covmat))

# Extract vector of estimated compliance scores
cscore <- cscoreout$C.score
summary(cscore)