Iterates over all possible treated-control school pairs, optionally computes and stores an optimal student match for each one, and generates a distance matrix for schools based on the quality of each student match.

1 2 | ```
matchStudents(students, treatment, school.id, match.students,
student.vars, school.caliper = NULL, verbose, penalty.qtile, min.keep.pctg)
``` |

`students` |
a dataframe containing student covariates, with a different row for each student. |

`treatment` |
the column name of the binary treatment status indicator in the |

`school.id` |
the column name of the unique school ID in the |

`match.students` |
logical value. If |

`student.vars` |
column names of variables in |

`school.caliper` |
matrix with one row for each treated school and one column for each control school, containing zeroes for pairings allowed by the caliper and |

`verbose` |
a logical value indicating whether detailed output should be printed. |

`penalty.qtile` |
a numeric value between 0 and 1 specifying a quantile of the distribution of all student-student matching distances. The algorithm will prefer to exclude treated students rather than form pairs with distances exceeding this quantile. |

`min.keep.pctg` |
a minimum percentage of students in the smaller school in a pair which must be retained, even when treated students are excluded. |

The `penalty.qtile`

and `min.keep.pctg`

control the rate at which students are trimmed from the match. If the quantile is high enough no students should be excluded in any match; if the quantile is very low the `min.keep.pctg`

can still ensure a minimal sample size in each match.

A list with two elements:

`student.matches` |
a list with one element for each treated school. Each element is a list with one element for each control school, and each element of these secondary lists is a dataframe containing the matched sample for the corresponding treated-control pairing. |

`schools.matrix` |
a matrix with one row for each treated school and one column for each control school, giving matching distances based on the student match. |

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of Pennsylvania, spi@wharton.upenn.edu

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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