DisC2o.AIPWestimate

DisC2o AIPW estimate of the ATE at each site

internal

A collection of privacy-preserving distributed algorithms (PDAs) for conducting federated statistical learning across multiple data sites. The PDA framework includes models for various tasks such as regression, trial emulation, causal inference, design-specific analysis, and clustering. The PDA algorithms run on a lead site and only require summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online data transfer system (<https://pda-ota.pdamethods.org/>) for safe and convenient collaboration. For more information, please visit our software websites: <https://github.com/Penncil/pda>, and <https://pdamethods.org/>.

Chongliang Luo

Privacy-Preserving Distributed Algorithms

Rui Duan

Mackenzie Edmondson

Jiayi Tong

Xiaokang Liu

Kenneth Locke

Jie Hu

Bingyu Zhang

Yicheng Shen

Yudong Wang

Yiwen Lu

Lu Li

Yong Chen

Penn Computing Inference Learning (PennCIL) lab 

DisC2o.AIPWestimate function

<dl><dt>ipdata</dt>
<dd>local data in data frame</dd>
<dt>control</dt>
<dd>pda control</dd>
<dt>config</dt>
<dd>pda cloud configuration</dd></dl>

Arguments

DisC2o AIPW estimate of the ATE at each site — DisC2o.AIPWestimate

<dl>

<dt>ipdata</dt>
<dd>local data in data frame</dd>


<dt>control</dt>
<dd>pda control</dd>


<dt>config</dt>
<dd>pda cloud configuration</dd>

</dl>

DisC2o AIPW estimate of the ATE at each site

DisC2o.AIPWestimate: DisC2o AIPW estimate of the ATE at each site

Description

Usage

Value

Arguments