konfound

Quantify the Robustness of Causal Inferences

Statistical methods that quantify the conditions necessary to alter inferences, also known as sensitivity analysis, are becoming increasingly important to a variety of quantitative sciences. A series of recent works, including Frank (2000) <doi:10.1177/0049124100029002001> and Frank et al. (2013) <doi:10.3102/0162373713493129> extend previous sensitivity analyses by considering the characteristics of omitted variables or unobserved cases that would change an inference if such variables or cases were observed. These analyses generate statements such as "an omitted variable would have to be correlated at xx with the predictor of interest (e.g., treatment) and outcome to invalidate an inference of a treatment effect". Or "one would have to replace pp percent of the observed data with null hypothesis cases to invalidate the inference". We implement these recent developments of sensitivity analysis and provide modules to calculate these two robustness indices and generate such statements in R. In particular, the functions konfound(), pkonfound() and mkonfound() allow users to calculate the robustness of inferences for a user's own model, a single published study and multiple studies respectively.

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Description file content

Type
Package
Package
konfound
Title
Quantify the Robustness of Causal Inferences
Version
0.1.2
Description
Statistical methods that quantify the conditions necessary to alter inferences, also known as sensitivity analysis, are becoming increasingly important to a variety of quantitative sciences. A series of recent works, including Frank (2000) and Frank et al. (2013) extend previous sensitivity analyses by considering the characteristics of omitted variables or unobserved cases that would change an inference if such variables or cases were observed. These analyses generate statements such as "an omitted variable would have to be correlated at xx with the predictor of interest (e.g., treatment) and outcome to invalidate an inference of a treatment effect". Or "one would have to replace pp percent of the observed data with null hypothesis cases to invalidate the inference". We implement these recent developments of sensitivity analysis and provide modules to calculate these two robustness indices and generate such statements in R. In particular, the functions konfound(), pkonfound() and mkonfound() allow users to calculate the robustness of inferences for a user's own model, a single published study and multiple studies respectively.
License
MIT + file LICENSE
Imports
broom, dplyr, ggplot2, margins, pbkrtest, purrr, rlang, tidyr
Suggests
devtools, forcats, knitr, lme4, rmarkdown, roxygen2, testthat, mice
VignetteBuilder
knitr
Encoding
UTF-8
LazyData
true
RoxygenNote
6.1.1
URL
BugReports
https://github.com/jrosen48/konfound/issues
NeedsCompilation
no
Packaged
2019-04-12 11:35:43 UTC; joshuarosenberg
Author
Joshua M Rosenberg [aut, cre], Ran Xu [ctb], Kenneth A Frank [ctb]
Maintainer
Joshua M Rosenberg
Repository
CRAN
Date/Publication
2019-04-12 11:52:40 UTC

install.packages('konfound')

0.1.2

12 days ago

https://github.com/jrosen48/konfound

Joshua M Rosenberg

MIT + file LICENSE

Imports

broom, dplyr, ggplot2, margins, pbkrtest, purrr, rlang, tidyr

Suggests

devtools, forcats, knitr, lme4, rmarkdown, roxygen2, testthat, mice

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