test_itr

Provides various statistical methods for evaluating heterogeneous treatment effects (HTE) in randomized experiments. The package includes tools to estimate uniform confidence bands for estimation of the group average treatment effect sorted by generic machine learning algorithms (GATES). It also provides the tools to identify a subgroup of individuals who are likely to benefit from a treatment the most "exceptional responders" or those who are harmed by it. Detailed reference in Imai and Li (2023) <doi:10.48550/arXiv.2310.07973>.

Michael Lingzhi Li

evalHTE

Evaluating Heterogeneous Treatment Effects

Kosuke Imai

Jialu Li

test_itr function

<dl><dt>model</dt>
<dd>Fitted model. Usually an output from <code>evaluate_hte</code>.</dd>
<dt>nsim</dt>
<dd>Number of Monte Carlo simulations used to simulate the null distributions. Default is 1000.</dd>
<dt>...</dt>
<dd>Further arguments passed to the function.</dd></dl>

Arguments

Conduct hypothesis tests — test_itr

<dl>

<dt>model</dt>
<dd>Fitted model. Usually an output from <code>evaluate_hte</code>.</dd>


<dt>nsim</dt>
<dd>Number of Monte Carlo simulations used to simulate the null distributions. Default is 1000.</dd>


<dt>...</dt>
<dd>Further arguments passed to the function.</dd>

</dl>

test_itr: Conduct hypothesis tests

Description

Usage

Value

Arguments