Omitted-variable bias analysis (sensemakr framework) — omitted_variable

Closed-form Cinelli-Hazlett robustness-value implementation in base R. For the full sensemakr treatment (benchmark plots, adjusted t-statistics, contour plots) on a fitted lm object, use morie_sensitivity_omitted_var_bias.

Usage

omitted_variable_bias(
  estimate,
  se,
  dof,
  r2_yd_x,
  partial_r2_treatment,
  q = 1,
  alpha = 0.05,
  benchmark_covariates = NULL
)

Arguments

estimate: Treatment coefficient.
se: SE of the estimate.
dof: Residual degrees of freedom.
r2_yd_x: Partial R^2 of treatment with outcome.
partial_r2_treatment: Same as r2_yd_x (for clarity).
q: Fraction of the estimate to be explained away. Default 1.
alpha: Significance level. Default 0.05.
benchmark_covariates: Named list mapping covariate name -> partial R^2.

Value

A morie_ovb named-list.

Examples

str(omitted_variable_bias(0.5, 0.15, dof = 150, r2_yd_x = 0.1,
                          partial_r2_treatment = 0.05), max.level = 1)
#> List of 7
#>  $ estimate            : num 0.5
#>  $ se                  : num 0.15
#>  $ rv_q                : num 0.244
#>  $ rv_qa               : num 0.871
#>  $ partial_r2_treatment: num 0.05
#>  $ benchmark_bounds    : list()
#>  $ interpretation      : chr "To explain away 100% of the estimate (0.5000), an unobserved confounder would need partial R^2 >= 0.2441 with b"| __truncated__
#>  - attr(*, "class")= chr [1:2] "morie_ovb" "list"