classic.neyman

This function estimates the sample average treatment effect for a set of
matched pairs using the classic Neyman estimator. For references on the
classic Neyman estimator, see baiocchi2010building,zhang2022bridging,heng2023instrumental;textualnbpInference

Conduct inference on the sample average treatment effect for a
matched (observational) dataset with a continuous treatment. Equipped with
calipered non-bipartite matching, bias-corrected sample average treatment
effect estimation, and covariate-adjusted variance estimation. Matching,
estimation, and inference methods are described in
Frazier, Heng and Zhou (2024) <doi:10.48550/arXiv.2409.11701>.

Anthony Frazier

nbpInference

Inference on Average Treatment Effects for Continuous Treatments

Siyu Heng

Wen Zhou

classic.neyman function

<dl><dt>Y</dt>
<dd>a 2I-length vector of outcome values, which must be numeric.</dd>
<dt>Z</dt>
<dd>a 2I-length vector of treatment values, which must be numeric.</dd>
<dt>pairs</dt>
<dd>an I x 2 dataframe containing the indices of observations
that form our set of matched pairs. An appropriate pairs dataframe can be
formed using the nbp.caliper function.</dd></dl>

Arguments

Classic Neyman Sample Average Treatment Effect Estimator — classic.neyman

<dl>

<dt>Y</dt>
<dd>a 2I-length vector of outcome values, which must be numeric.</dd>


<dt>Z</dt>
<dd>a 2I-length vector of treatment values, which must be numeric.</dd>


<dt>pairs</dt>
<dd>an I x 2 dataframe containing the indices of observations
that form our set of matched pairs. An appropriate pairs dataframe can be
formed using the nbp.caliper function.</dd>

</dl>

classic.neyman: Classic Neyman Sample Average Treatment Effect Estimator

Description

Usage

Value

Arguments

See Also

Examples