42.3 Omitted Variable Bias Quantification
To quantify the bias needed to change the substantive conclusion from a causal inference study.
library(konfound)
pkonfound(
est_eff = 5,
std_err = 2,
n_obs = 1000,
n_covariates = 5
)
#> Robustness of Inference to Replacement (RIR):
#> RIR = 215
#>
#> To invalidate the inference of an effect using the threshold of 3.925 for
#> statistical significance (with null hypothesis = 0 and alpha = 0.05), 21.506%
#> of the (5) estimate would have to be due to bias. This implies that to
#> invalidate the inference one would expect to have to replace 215 (21.506%)
#> observations with data points for which the effect is 0 (RIR = 215).
#>
#> 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.
pkonfound(
est_eff = 5,
std_err = 2,
n_obs = 1000,
n_covariates = 5,
to_return = "thresh_plot"
)
