The goal of `konfound`

is to carry out sensitivity
analysis to help analysts to *quantify how robust inferences are to
potential sources of bias*. This R package provides tools to carry
out sensitivity analysis as described in Frank, Maroulis, Duong, and
Kelcey (2013) based on Rubin’s (1974) causal model as well as in Frank
(2000) based on the impact threshold for a confounding variable.

You can install the CRAN version of konfound with:

`install.packages("konfound")`

You can install the development version from GitHub with:

```
install.packages("devtools")
::install_github("konfound-project/konfound") devtools
```

`pkonfound()`

, for published studies, calculates (1) how
much bias there must be in an estimate to invalidate/sustain an
inference, and interprets in terms of how much data would need to be
replaced to nullify an inference; (2) the impact of an omitted variable
necessary to invalidate/sustain an inference for a regression
coefficient:

```
library(konfound)
#> Sensitivity analysis as described in Frank,
#> Maroulis, Duong, and Kelcey (2013) and in
#> Frank (2000).
#> For more information visit http://konfound-it.com.
```

```
pkonfound(est_eff = 2,
std_err = .4,
n_obs = 100,
n_covariates = 3)
#> Robustness of Inference to Replacement (RIR):
#> RIR = 60
#>
#> To invalidate the inference of an effect using the threshold of 0.794 for
#> statistical significance (with null hypothesis = 0 and alpha = 0.05), 60.295%
#> of the (2) estimate would have to be due to bias. This implies that to
#> invalidate the inference one would expect to have to replace 60 (60.295%)
#> observations with data points for which the effect is 0 (RIR = 60).
#>
#> See Frank et al. (2013) for a description of the method.
#>
#> Citation: Frank, K.A., Maroulis, S., Duong, M., and Kelcey, B. (2013).
#> What would it take to change an inference?
#> Using Rubin's causal model to interpret the robustness of causal inferences.
#> Education, Evaluation and Policy Analysis, 35 437-460.
#>
#> Accuracy of results increases with the number of decimals reported.
#> For other forms of output, run
#> ?pkonfound and inspect the to_return argument
#> For models fit in R, consider use of konfound().
```

`konfound()`

calculates the robustness of inferences for
models fit in R. For example, below are the coefficients for a linear
model fit with `lm()`

using the built-in dataset
`mtcars`

:

```
<- lm(mpg ~ wt + disp, data = mtcars)
m1
m1#>
#> Call:
#> lm(formula = mpg ~ wt + disp, data = mtcars)
#>
#> Coefficients:
#> (Intercept) wt disp
#> 34.96055 -3.35083 -0.01772
summary(m1)
#>
#> Call:
#> lm(formula = mpg ~ wt + disp, data = mtcars)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -3.4087 -2.3243 -0.7683 1.7721 6.3484
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 34.96055 2.16454 16.151 4.91e-16 ***
#> wt -3.35082 1.16413 -2.878 0.00743 **
#> disp -0.01773 0.00919 -1.929 0.06362 .
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 2.917 on 29 degrees of freedom
#> Multiple R-squared: 0.7809, Adjusted R-squared: 0.7658
#> F-statistic: 51.69 on 2 and 29 DF, p-value: 2.744e-10
```

Sensitivity analysis for the effect for `wt`

on
`mpg`

can be carried out as follows, specifying the fitted
model object:

```
konfound(m1, wt)
#> Robustness of Inference to Replacement (RIR):
#> RIR = 9
#>
#> To invalidate the inference of an effect using the threshold of -2.381 for
#> statistical significance (with null hypothesis = 0 and alpha = 0.05), 28.946%
#> of the (-3.351) estimate would have to be due to bias. This implies that to
#> invalidate the inference one would expect to have to replace 9 (28.946%)
#> observations with data points for which the effect is 0 (RIR = 9).
#>
#> See Frank et al. (2013) for a description of the method.
#>
#> Citation: Frank, K.A., Maroulis, S., Duong, M., and Kelcey, B. (2013).
#> What would it take to change an inference?
#> Using Rubin's causal model to interpret the robustness of causal inferences.
#> Education, Evaluation and Policy Analysis, 35 437-460.
#>
#> Accuracy of results increases with the number of decimals reported.
#> NULL
```

`mkonfound()`

supports sensitivity that can be compared or
synthesized across multiple analyses. We can use an existing (and
built-in) dataset, such as `mkonfound_ex`

.

```
mkonfound_ex#> # A tibble: 30 × 2
#> t df
#> <dbl> <dbl>
#> 1 7.08 178
#> 2 4.13 193
#> 3 1.89 47
#> 4 -4.17 138
#> 5 -1.19 97
#> 6 3.59 87
#> 7 0.282 117
#> 8 2.55 75
#> 9 -4.44 137
#> 10 -2.05 195
#> # ℹ 20 more rows
mkonfound(mkonfound_ex, t, df)
#> # A tibble: 30 × 7
#> t df action inference pct_bias_to_change_i…¹ itcv r_con
#> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 7.08 178 to_invalidate reject_null 68.8 0.378 0.614
#> 2 4.13 193 to_invalidate reject_null 50.6 0.168 0.41
#> 3 1.89 47 to_sustain fail_to_rejec… 5.47 -0.012 0.11
#> 4 -4.17 138 to_invalidate reject_null 50.3 0.202 0.449
#> 5 -1.19 97 to_sustain fail_to_rejec… 39.4 -0.065 0.255
#> 6 3.59 87 to_invalidate reject_null 41.9 0.19 0.436
#> 7 0.282 117 to_sustain fail_to_rejec… 85.5 -0.131 0.361
#> 8 2.55 75 to_invalidate reject_null 20.6 0.075 0.274
#> 9 -4.44 137 to_invalidate reject_null 53.0 0.225 0.475
#> 10 -2.05 195 to_invalidate reject_null 3.51 0.006 0.077
#> # ℹ 20 more rows
#> # ℹ abbreviated name: ¹pct_bias_to_change_inference
```

To learn more about sensitivity analysis, please visit:

- The KonFound-It website, with latest news, links to tools and support
- The Introduction
to konfound vignette, with detailed information about each of the
functions (
`pkonfound()`

,`konfound()`

, and`mkounfound()`

) - The Konfound-It! interactive web application, with links to PowerPoints and key publications

We prefer for issues to be filed via GitHub (link to the issues page
for `konfound`

here)
though we also welcome questions or feedback requests via email (see the
DESCRIPTION file).

Contributing guidelines are here.

Please note that the konfound project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.