parse_and_prep

accepts formula object and data, creates the data used for refinement

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

parse_and_prep function

<dl><dt>formula</dt>
<dd>formula object specifying how to construct the data used for refinement. This is likely to be some variation of the covs.formula argument.</dd>
<dt>data</dt>
<dd>data.frame object to be used to create the data needed for refinement. data has unit, time, treatment columns in that order, followed by everything else</dd></dl>

Arguments

parse_and_prep — parse_and_prep

<dl>

<dt>formula</dt>
<dd>formula object specifying how to construct the data used for refinement. This is likely to be some variation of the covs.formula argument.</dd>


<dt>data</dt>
<dd>data.frame object to be used to create the data needed for refinement. data has unit, time, treatment columns in that order, followed by everything else</dd>

</dl>

parse_and_prep: parse_and_prep

Description

Usage

Value

Arguments