build_maha_mats

build_maha_mats
Builds the matrices that we will then use to calculate the mahalanobis distances for each matched set

internal

Implements a set of methodological tools
that enable researchers to apply matching methods to
time-series cross-sectional data. Imai, Kim, and Wang
(2023) <http://web.mit.edu/insong/www/pdf/tscs.pdf>
proposes a nonparametric generalization of the
difference-in-differences estimator, which does not rely
on the linearity assumption as often done in
practice. Researchers first select a method of matching
each treated observation for a given unit in a
particular time period with control observations from
other units in the same time period that have a similar
treatment and covariate history. These methods include
standard matching methods based on propensity score and
Mahalanobis distance, as well as weighting methods. Once
matching and refinement is done,
treatment effects can be estimated with
standard errors. The package also offers diagnostics for researchers to assess the quality
of their results.

In Kim

PanelMatch

Matching Methods for Causal Inference with Time-Series
Cross-Sectional Data

In Song Kim

Adam Rauh

Erik Wang

Kosuke Imai

build_maha_mats function

<dl><dt>idx</dt>
<dd>List of vectors specifying which observations should be extracted</dd>
<dt>ordered_expanded_data</dt>
<dd>data.frame of prepared/parsed input data</dd></dl>

Arguments

build_maha_mats
Builds the matrices that we will then use to calculate the mahalanobis distances for each matched set — build_maha_mats

<dl>

<dt>idx</dt>
<dd>List of vectors specifying which observations should be extracted</dd>


<dt>ordered_expanded_data</dt>
<dd>data.frame of prepared/parsed input data</dd>

</dl>

build_maha_mats: build_maha_mats Builds the matrices that we will then use to calculate the mahalanobis distances for each matched set

Description

Usage

Value

Arguments