match_2C_mat: Perform a pair matching using two user-specified distance...

In match2C: Match One Sample using Two Criteria

Perform a pair matching using two user-specified distance matrices.

Description

This function performs a pair-matching using two user-specified distance matrices and two calipers. Typically one distance matrix is used to minimize matched-pair differences, and a second distance matrix is used to enforce constraints on marginal distributions of certain variables.

Usage

match_2C_mat(
  Z,
  dataset,
  dist_mat_1,
  dist_mat_2,
  lambda,
  controls = 1,
  p_1 = NULL,
  caliper_1 = NULL,
  k_1 = NULL,
  p_2 = NULL,
  caliper_2 = NULL,
  k_2 = NULL,
  penalty = Inf,
  overflow = FALSE
)

Arguments

Z	A length-n vector of treatment indicator.
dataset	The original dataset.
dist_mat_1	A user-specified treatment-by-control (n_t-by-n_c) distance matrix.
dist_mat_2	A second user-specified treatment-by-control (n_t-by-n_c) distance matrix.
lambda	A penalty that controls the trade-off between two parts of the network.
controls	Number of controls matched to each treated.
p_1	A length-n vector on which caliper_1 applies, e.g. a vector of propensity score.
caliper_1	Size of caliper_1.
k_1	Maximum number of controls each treated is connected to in the first network.
p_2	A length-n vector on which caliper_2 applies, e.g. a vector of propensity score.
caliper_2	Size of caliper_2.
k_2	Maximum number of controls each treated is connected to in the second network.
penalty	Penalty for violating the caliper. Set to Inf by default.
overflow	A logical value indicating if overflow protection is turned on.

Details

This function performs a pair matching via a two-part network. The first part is a network whose treatment-to-control distance matrix is supplied by dist_mat_1. The second part of the network is constructed using distance matrix specified by dist_mat_2. Often, the first part of the network is used to minimize total treated-to-control matched pair distances, and the second part is used to enforce certain marginal constraints.

The function constructs two list representations of distance matrices, possibly using the caliper. caliper_1 is applied to p_1 (caliper_2 applied to p_2) in order to construct sparse list representations. For instance, a caliper equal to 0.2 (caliper_1 = 0.2) applied to the propensity score (p_1).

lambda is a penalty, or a tuning parameter, that balances these two objectives. When lambda is very large, the network will first minimize the second part of network and then the first part.

Value

This function returns a list of three objects including the feasibility of the matching problem and the matched controls organized in different formats. See the documentation of the function construct_outcome or the tutorial for more details.

Examples

## Not run: 
To run the following code, one needs to first install
and load the package optmatch.

# We first prepare the input X, Z, propensity score

#attach(dt_Rouse)
#X = cbind(female,black,bytest,dadeduc,momeduc,fincome)
#Z = IV
#propensity = glm(IV~female+black+bytest+dadeduc+momeduc+fincome,
#family=binomial)$fitted.values
#n_t = sum(Z)
#n_c = length(Z) - n_t
#dt_Rouse$propensity = propensity
#detach(dt_Rouse)

# Next, we use the match_on function in optmatch
to create two treated-by-control distance matrices.

#library(optmatch)
# dist_mat_1 = match_on(IV~female+black+bytest+dadeduc+momeduc+fincome,
# method = 'mahalanobis', data = dt_Rouse)

# dist_mat_2 = match_on(IV ~ female, method = 'euclidean', data = dt_Rouse)


# Feed two distance matrices to the function match_2C_mat without caliper
# and a large penalty lambda to enforce (near-)fine balance.

#matching_output = match_2C_mat(Z, dt_Rouse, dist_mat_1, dist_mat_2,
#                               lambda = 10000, p_1 = NULL, p_2 = NULL)

# For more examples, please consult the RMarkdown tutorial.

## End(Not run)

match2C documentation built on March 31, 2023, 6:39 p.m.