YOPData

Data from a re-analysis of the Youth Opportunities Program anti-poverty RCT 
in Uganda, with satellite imagery neural representations linked to RCT units.

Provides hardware-accelerated tools for performing rerandomization
and randomization testing in experimental research. Using a 'JAX' backend, the
package enables exact rerandomization inference even for large experiments
with hundreds of billions of possible randomizations. Key functionalities
include generating pools of acceptable rerandomizations based on covariate
balance, conducting exact randomization tests, and performing pre-analysis
evaluations to determine optimal rerandomization acceptance thresholds. The
package supports various hardware acceleration frameworks including 'CPU',
'CUDA', and 'METAL', making it versatile across accelerated computing environments. This
allows researchers to efficiently implement stringent rerandomization designs and
conduct valid inference even with large sample sizes. The package is partly based on Jerzak and Goldstein (2023) <doi:10.48550/arXiv.2310.00861>.

Connor Jerzak

fastrerandomize

Hardware-Accelerated Rerandomization for Improved Balance

Fucheng Warren Zhu

Aniket Sachin Kamat

Rebecca Goldstein

YOPData function

A list containing two data frames:<dl>
 <dt>RCTData</dt>
<dd>Treatment, outcome, and geolocation information</dd> <dt>ImageEmbeddings</dt>
<dd>CLIP-RSICD neural embeddings of satellite imagery</dd>
</dl>

Format

YOPData — YOPData

A list containing two data frames:<dl>
 <dt>RCTData</dt>
<dd>Treatment, outcome, and geolocation information</dd>

 <dt>ImageEmbeddings</dt>
<dd>CLIP-RSICD neural embeddings of satellite imagery</dd>


</dl>

YOPData: YOPData

Description

Usage

Arguments

Format