BioSample

<p>This data set gives a small sample of the data used in ``Discovery of Treatments from Text Corpora'' by Christian Fong and Justin Grimmer.  This sample is intended as a toy data set for use in the examples of this package's documentation.  A real data set should include far more observations.</p>

datasets

Implements the approach described in Fong and Grimmer (2016) <https://aclweb.org/anthology/P/P16/P16-1151.pdf> for
automatically discovering latent treatments from a corpus and estimating the average marginal component effect (AMCE) of
each treatment.  The data is divided into a training and test set.  The supervised Indian Buffet Process (sibp) is used
to discover latent treatments in the training set.  The fitted model is then applied to the test set to infer the values
of the latent treatments in the test set.  Finally, Y is regressed on the latent treatments in the test set to estimate
the causal effect of each treatment.

Christian Fong

texteffect

Discovering Latent Treatments in Text Corpora and Estimating
Their Causal Effects

BioSample function

<p>A data frame consisting of 51 columns (including an outcome measure and counts for each word in a 50 word vocabulary) and 250 observations.</p>

BioSample: Sample from the Fong and Grimmer Wikipedia Biography Data

Description

Usage

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References