Discovering Latent Treatments in Text Corpora and Estimating Their Causal Effects
Overview
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.
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- NOTE14 NOTE2026-03-10
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- 7
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- archivedRemoved from CRAN2026-05-06email to the maintainer is undeliverable
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- 0.32019-03-24 · diff ↗
- 0.2
- RR 3.5.0 released · 2018-04-23
- 0.12017-11-27
- RR 3.4.0 released · 2017-04-21
Package metadata
- First published
- 2017-11-27
- Total releases
- 3 / 9 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.3
- Bundled data
- 7.1 KB / 1 file
- Download size
- 19 KB
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