clean_leads

clean_leads
Function to check the lead windows in treated and control units for missing outcome data. If data is missing, remove those units from matched sets.

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

clean_leads function

<dl><dt>matched_sets</dt>
<dd>matched.set object contained pre-filtered matched sets</dd>
<dt>ordered.data</dt>
<dd>data.frame object to be checked for missing data. This should have been passed through data preparation functions already.</dd>
<dt>max.lead</dt>
<dd>Integer specifying the biggest value of the lead window.</dd>
<dt>t.var</dt>
<dd>string specifying the time id variable</dd>
<dt>id.var</dt>
<dd>string specifying the unit id variable</dd>
<dt>outcome.var</dt>
<dd>string specifying the outcome variable.</dd></dl>

Arguments

clean_leads
Function to check the lead windows in treated and control units for missing outcome data. If data is missing, remove those units from matched sets. — clean_leads

<dl>

<dt>matched_sets</dt>
<dd>matched.set object contained pre-filtered matched sets</dd>


<dt>ordered.data</dt>
<dd>data.frame object to be checked for missing data. This should have been passed through data preparation functions already.</dd>


<dt>max.lead</dt>
<dd>Integer specifying the biggest value of the lead window.</dd>


<dt>t.var</dt>
<dd>string specifying the time id variable</dd>


<dt>id.var</dt>
<dd>string specifying the unit id variable</dd>


<dt>outcome.var</dt>
<dd>string specifying the outcome variable.</dd>

</dl>

clean_leads
Function to check the lead windows in treated and control units for missing outcome data. If data is missing, remove those units from matched sets.

clean_leads: clean_leads Function to check the lead windows in treated and control units for missing outcome data. If data is missing, remove those units from matched sets.

Description

Usage

Value

Arguments