README.md
In zhangly811/MvDeconfounder: Deconfounder
Deconfounder
Deconfounder is an R package for building and validating the medical deconfounder using data in the OHDSI OMOP Common Data Model format.
Introduction
Deconfounder is a causal inference model for estimating the treatment effects of medications with eletronic health records. We aim to assess the treatment effects of multiple medications on measurements using the Deconfounder. Deconfounder identifies the causal medications that have either direct effect or adverse effect on each clinical measurement. The inputs to Deconfounder are medication records and pre-treatment and post-treatment measurement values. The Deconfounder fits a probabilistic factor model (e.g., poisson factoriztion or deep exponential family) to the medication records to construct substitute confounders and adjusts for substitute confounders in the outcome model for assessing the causal effects of medications.
Technology
Deconfounder is an R package, with some functions implemented in python.
System Requirements
Requires R (version 3.4.0 or higher), Python (version 3.7 or higher) and Java.
Dependencies
OHDSI R packages:
Python packages:
-
torch==1.6.0
-
numpy
-
pandas
-
scipy
-
sklearn
Installation
Install from github using devtools package.
install.packages("devtools")
devtools::install_github("zhangly811/Deconfounder")
User Documentation
Vignette: Deconfounder on single outcome
Manual: Deconfounder
License
Deconfounder is licensed under Apache License 2.0
Development
Deconfounder is being developed in R Studio.
References
Zhang L, Wang Y, Ostropolets A, Mulgrave JJ, Blei DM, Hripcsak G. The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records. Accepted at Machine Learning for Health Care (MLHC) Conference 2019.
Y. Wang and D. Blei. The blessings of multiple causes. Journal of the American Statistical Association, 114:528, 1574-1596, 2019.
zhangly811/MvDeconfounder documentation built on Jan. 21, 2021, 12:11 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Deconfounder
Deconfounder is an R package for building and validating the medical deconfounder using data in the OHDSI OMOP Common Data Model format.
Introduction
Deconfounder is a causal inference model for estimating the treatment effects of medications with eletronic health records. We aim to assess the treatment effects of multiple medications on measurements using the Deconfounder. Deconfounder identifies the causal medications that have either direct effect or adverse effect on each clinical measurement. The inputs to Deconfounder are medication records and pre-treatment and post-treatment measurement values. The Deconfounder fits a probabilistic factor model (e.g., poisson factoriztion or deep exponential family) to the medication records to construct substitute confounders and adjusts for substitute confounders in the outcome model for assessing the causal effects of medications.
Technology
Deconfounder is an R package, with some functions implemented in python.
System Requirements
Requires R (version 3.4.0 or higher), Python (version 3.7 or higher) and Java.
Dependencies
OHDSI R packages:
Python packages:
-
torch==1.6.0
-
numpy
-
pandas
-
scipy
-
sklearn
Installation
Install from github using devtools package.
install.packages("devtools")
devtools::install_github("zhangly811/Deconfounder")
User Documentation
Vignette: Deconfounder on single outcome
Manual: Deconfounder
License
Deconfounder is licensed under Apache License 2.0
Development
Deconfounder is being developed in R Studio.
References
Zhang L, Wang Y, Ostropolets A, Mulgrave JJ, Blei DM, Hripcsak G. The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records. Accepted at Machine Learning for Health Care (MLHC) Conference 2019.
Y. Wang and D. Blei. The blessings of multiple causes. Journal of the American Statistical Association, 114:528, 1574-1596, 2019.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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