Additional Arguments for Specification of Distance Measures

The following arguments specify distance measures that are used for matching methods. These arguments apply to all matching methods except exact matching.

• distance: the method used to estimate the distance measure (default = "logit", logistic regression) or a numerical vector of user's own distance measure. Before using any of these techniques, it is best to understand the theoretical groundings of these techniques and to evaluate the results. Most of these methods (such as logistic or probit regression) define the distance by first estimating the propensity score, defined as the probability of receiving treatment, conditional on the covariates. Available methods include:
  • "mahalanobis": the Mahalanobis distance measure.
  • binomial generalized linear models with one of the following link functions:
    • "logit": logistic link
    • "linear.logit": logistic link with linear propensity score^4.1
    • "probit": probit link
    • "linear.probit": probit link with linear propensity score
    • "cloglog": complementary log-log link
    • "linear.cloglog": complementary log-log link with linear propensity score
    • "log": log link
    • "linear.log": log link with linear propensity score
    • "cauchit" Cauchy CDF link
    • "linear.cauchit" Cauchy CDF link with linear propensity score
  • Choose one of the following generalized additive models (see help(gam) for more options).
    • "GAMlogit": logistic link
    • "GAMlinear.logit": logistic link with linear propensity score
    • "GAMprobit": probit link
    • "GAMlinear.probit": probit link with linear propensity score
    • "GAMcloglog": complementary log-log link
    • "GAMlinear.cloglog": complementary log-log link with linear propensity score
    • "GAMlog": log link
    • "GAMlinear.log": log link with linear propensity score,
    • "GAMcauchit": Cauchy CDF link
    • "GAMlinear.cauchit": Cauchy CDF link with linear propensity score
  • "nnet": neural network model. See help(nnet) for more options.
  • "rpart": classification trees. See help(rpart) for more options.

• distance.options: optional arguments for estimating the distance measure. The input to this argument should be a list. For example, if the distance measure is estimated with a logistic regression, users can increase the maximum IWLS iterations by distance.options = list(maxit = 5000). Find additional options for general linear models using help(glm) or help(family), for general additive models using help(gam), for neutral network models help(nnet), and for classification trees help(rpart).

• discard: specifies whether to discard units that fall outside some measure of support of the distance measure (default = "none", discard no units). Discarding units may change the quantity of interest being estimated by changing the observations left in the analysis. Enter a logical vector indicating which unit should be discarded or choose from the following options:
  • "none": no units will be discarded before matching. Use this option when the units to be matched are substantially similar, such as in the case of matching treatment and control units from a field experiment that was close to (but not fully) randomized (e.g., ), when caliper matching will restrict the donor pool, or when you do not wish to change the quantity of interest and the parametric methods to be used post-matching can be trusted to extrapolate.
  • "hull.both": all units that are not within the convex hull will be discarded. See (,) for information about the convex hull in this context and as a measure of model dependence.
  • "both": all units (treated and control) that are outside the support of the distance measure will be discarded.
  • "hull.control": only control units that are not within the convex hull of the treated units will be discarded.
  • "control": only control units outside the support of the distance measure of the treated units will be discarded. Use this option when the average treatment effect on the treated is of most interest and when you are unwilling to discard non-overlapping treatment units (which would change the quantity of interest).
  • "hull.treat": only treated units that are not within the convex hull of the control units will be discarded.
  • "treat": only treated units outside the support of the distance measure of the control units will be discarded. Use this option when the average treatment effect on the control units is of most interest and when unwilling to discard control units.

• reestimate: If FALSE (default), the model for the distance measure will not be re-estimated after units are discarded. The input must be a logical value. Re-estimation may be desirable for efficiency reasons, especially if many units were discarded and so the post-discard samples are quite different from the original samples.