simDat4

This dataset is used in several documentation files to illustrate the
different functionalities of the package.

datasets

Several causal effects are measured using least squares regressions and basis function approximations. Backward and forward selection methods based on different criteria are used to select the basis functions.

Pierre Chausse

causalSLSE

Semiparametric Least Squares Inference for Causal Effects

Pierre Chausse Developer

Mihai Giurcanu Developer

simDat4 function

A data frame with 500 observations on the following 7 variables.<dl>
 <dt><code>Z</code></dt>
<dd>Treatment indicator.</dd> <dt><code>Y</code></dt>
<dd>Observed outcome.</dd> <dt><code>X1</code></dt>
<dd>Continuous covariates with a large proportion of
	zeros.</dd> <dt><code>X2</code></dt>
<dd>A categorical variable with 3 character levels (not expressed
 as factor).</dd> <dt><code>X3</code></dt>
<dd>A categorical variable with 3 numerical levels (not expressed
 as factor).</dd> <dt><code>X4</code></dt>
<dd>A binary variable.</dd> <dt><code>treat</code></dt>
<dd>A character variable.</dd>
 
 
</dl>

Format

Simulated Data. — simDat4

A data frame with 500 observations on the following 7 variables.<dl>
 <dt><code>Z</code></dt>
<dd>Treatment indicator.</dd>

 <dt><code>Y</code></dt>
<dd>Observed outcome.</dd>

 <dt><code>X1</code></dt>
<dd>Continuous covariates with a large proportion of
	zeros.</dd>

 <dt><code>X2</code></dt>
<dd>A categorical variable with 3 character levels (not expressed
 as factor).</dd>

 <dt><code>X3</code></dt>
<dd>A categorical variable with 3 numerical levels (not expressed
 as factor).</dd>

 <dt><code>X4</code></dt>
<dd>A binary variable.</dd>

 <dt><code>treat</code></dt>
<dd>A character variable.</dd>
 
 
</dl>

simDat4: Simulated Data.

Description

Usage

Arguments

Format