match_2C_mat | R Documentation |

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.

```
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
)
```

`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. |

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.

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.

```
## 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)
```

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