summary.CausalANOVA

The heterogeneous treatment effect estimation procedure
proposed by Imai and Ratkovic (2013)<DOI: 10.1214/12-AOAS593>.
The proposed method is applicable, for
example, when selecting a small number of most (or least)
efficacious treatments from a large number of alternative
treatments as well as when identifying subsets of the
population who benefit (or are harmed by) a treatment of
interest. The method adapts the Support Vector Machine
classifier by placing separate LASSO constraints over the
pre-treatment parameters and causal heterogeneity parameters of
interest. This allows for the qualitative distinction between
causal and other parameters, thereby making the variable
selection suitable for the exploration of causal heterogeneity.
The package also contains a class of functions, CausalANOVA,
which estimates the average marginal interaction effects (AMIEs)
by a regularized ANOVA as proposed by Egami and Imai (2019).
It contains a variety of regularization techniques to facilitate
analysis of large factorial experiments.

Naoki Egami

FindIt

Finding Heterogeneous Treatment Effects

Marc Ratkovic

Kosuke Imai

summary.CausalANOVA function

<dl><dt>object</dt>
<dd>An object from <code>CausalANOVA</code></dd>
<dt>digit</dt>
<dd>the number of digits</dd>
<dt>verbose</dt>
<dd>report additional summary results</dd>
<dt>verbose.full</dt>
<dd>report full summary results</dd>
<dt>...</dt>
<dd>Other parameters</dd></dl>

Arguments

Summarizing CausalANOVA output — summary.CausalANOVA

<dl>

<dt>object</dt>
<dd>An object from <code>CausalANOVA</code></dd>


<dt>digit</dt>
<dd>the number of digits</dd>


<dt>verbose</dt>
<dd>report additional summary results</dd>


<dt>verbose.full</dt>
<dd>report full summary results</dd>


<dt>...</dt>
<dd>Other parameters</dd>

</dl>

summary.CausalANOVA: Summarizing CausalANOVA output

Description

Usage

Arguments