In social science (and educational) research, we often wish to understand how robust inferences about effects are to unobserved (or controlled for) covariates, possible problems with measurement, and other sources of bias. 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")
devtools::install_github("jrosen48/konfound")
pkonfound()
, for published studies, calculates (1) how much bias there must be in an estimate to invalidate/sustain 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://konfoundit.com.
pkonfound(est_eff = 2,
std_err = .4,
n_obs = 100,
n_covariates = 3)
#> Percent Bias Necessary to Invalidate the Inference:
#> To invalidate an inference, 60.3% of the estimate would have to be due to bias. This is based on a threshold of 0.794 for statistical significance (alpha = 0.05).
#> To invalidate an inference, 60 observations would have to be replaced with cases 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 437460.
#> Impact Threshold for a Confounding Variable:
#> The minimum impact to invalidate an inference for a null hypothesis of 0 effect is based on a correlation of 0.568 with the outcome and at 0.568 with the predictor of interest (conditioning on observed covariates) based on a threshold of 0.201 for statistical significance (alpha = 0.05).
#> Correspondingly the impact of an omitted variable (as defined in Frank 2000) must be 0.568 X 0.568 = 0.323 to invalidate an inference for a null hypothesis of 0 effect.
#> See Frank (2000) for a description of the method
#> Citation: Frank, K. 2000. Impact of a confounding variable on the inference of a regression coefficient. Sociological Methods and Research, 29 (2), 147194
#> 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 same for models fit in R. For example, here are the coefficients for a linear model fit with lm()
using the builtin dataset mtcars
:
m1 < lm(mpg ~ wt + hp, data = mtcars)
m1
#>
#> Call:
#> lm(formula = mpg ~ wt + hp, data = mtcars)
#>
#> Coefficients:
#> (Intercept) wt hp
#> 37.22727 3.87783 0.03177
summary(m1)
#>
#> Call:
#> lm(formula = mpg ~ wt + hp, data = mtcars)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> 3.941 1.600 0.182 1.050 5.854
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>t)
#> (Intercept) 37.22727 1.59879 23.285 < 2e16 ***
#> wt 3.87783 0.63273 6.129 1.12e06 ***
#> hp 0.03177 0.00903 3.519 0.00145 **
#> 
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 2.593 on 29 degrees of freedom
#> Multiple Rsquared: 0.8268, Adjusted Rsquared: 0.8148
#> Fstatistic: 69.21 on 2 and 29 DF, pvalue: 9.109e12
Sensitivity analysis for the effect for wt
on mpg
can be carried out as follows, specifying the fitted model object:
konfound(m1, wt)
#> Percent Bias Necessary to Invalidate the Inference:
#> To invalidate an inference, 66.629% of the estimate would have to be due to bias. This is based on a threshold of 1.294 for statistical significance (alpha = 0.05).
#> To invalidate an inference, 21 observations would have to be replaced with cases for which the effect is 0 (RIR = 21).
#> 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 437460.
#> Impact Threshold for a Confounding Variable:
#> The minimum impact to invalidate an inference for a null hypothesis of 0 effect is based on a correlation of 0.791 with the outcome and at 0.791 with the predictor of interest (conditioning on observed covariates) based on a threshold of 0.366 for statistical significance (alpha = 0.05).
#> Correspondingly the impact of an omitted variable (as defined in Frank 2000) must be 0.791 X 0.791 = 0.626 to invalidate an inference for a null hypothesis of 0 effect.
#> See Frank (2000) for a description of the method
#> Citation: Frank, K. 2000. Impact of a confounding variable on the inference of a regression coefficient. Sociological Methods and Research, 29 (2), 147194
#> For more detailed output, consider setting `to_return` to table
#> To consider other predictors of interest, consider setting `test_all` to TRUE.
We can use an existing dataset, such as the CSV file here.
d < read.csv("https://msu.edu/~kenfrank/example%20dataset%20for%20mkonfound.csv")
head(d)
#> t df
#> 1 7.076763 178
#> 2 4.127893 193
#> 3 1.893137 47
#> 4 4.166395 138
#> 5 1.187599 97
#> 6 3.585478 87
mkonfound(d, t, df)
#> # A tibble: 30 x 7
#> t df action inference pct_bias_to_change_inf… itcv r_con
#> <dbl> <int> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 7.08 178 to_invalid… reject_null 68.8 0.378 0.614
#> 2 4.13 193 to_invalid… reject_null 50.6 0.168 0.41
#> 3 1.89 47 to_sustain fail_to_reject… 5.47 0.012 0.11
#> 4 4.17 138 to_invalid… reject_null 50.3 0.202 0.449
#> 5 1.19 97 to_sustain fail_to_reject… 39.4 0.065 0.255
#> 6 3.59 87 to_invalid… reject_null 41.9 0.19 0.436
#> 7 0.282 117 to_sustain fail_to_reject… 85.5 0.131 0.361
#> 8 2.55 75 to_invalid… reject_null 20.6 0.075 0.274
#> 9 4.44 137 to_invalid… reject_null 53.0 0.225 0.475
#> 10 2.05 195 to_invalid… reject_null 3.51 0.006 0.077
#> # … with 20 more rows
The above functions have a number of extensions; the below tables represent how pkonfound()
and konfound()
can be used:
Outcome  Predictor: Continuous  Predictor: Binary 

Continuous  pkonfound(est_eff, std_err, n_obs, n_covariates) 
pkonfound(est_eff, std_err, n_obs, n_covariates) 
Logistic  pkonfound(est_eff, std_err, n_obs, n_covariates, model_type = 'logistic') 
pkonfound(est_eff, std_err, n_obs, n_covariates, n_treat, model_type = 'logistic') or pkonfound(a, b, c, d)

Outcome  Predictor: Continuous  Predictor: Binary 

Continuous  konfound(m, var) 
konfound(m, var) 
Logistic  konfound(m, var) 
konfound(m, var, two_by_two = TRUE) 
Note that there are additional arguments for each of thes functions; see ?pkonfound()
or ?konfound()
for more details.
To learn more about sensitivity analysis, please visit:
pkonfound()
, konfound()
, and mkounfound()
)We prefer for issues to be filed via GitHub (link to the issues page for konfound
here) though we also welcome questions or feedback via email (see the DESCRIPTION file).
gPlease note that this project is released with a Contributor Code of Conduct available at http://contributorcovenant.org/version/1/0/0/