Rosenbaum bounds sensitivity analysis — morie_sensitivity

For a range of hidden-confounding levels \(\Gamma\), tests whether the treatment effect remains significant. A large \(\Gamma\) at which the result remains significant indicates robustness.

Usage

morie_sensitivity_rosenbaum(
  treated,
  control,
  gamma_range = seq(1, 3, by = 0.2)
)

Arguments

treated: Numeric vector of outcomes for treated units.
control: Numeric vector of outcomes for control units (may differ in length from treated for unmatched designs).
gamma_range: Numeric vector of \(\Gamma\) values to test.

Value

Data frame with columns: gamma, p_lower, p_upper.

Details

Delegates to rbounds::psens() when rbounds is installed and pairs-of-equal-length data are supplied; alternatively delegates to sensitivitymv::senmv() when sensitivitymv is installed. Otherwise falls back to inline sign-score bounds (Rosenbaum 2002, Section 4.3).

References

Rosenbaum PR (2002). Observational Studies (2nd ed.). Springer.

Examples

morie_sensitivity_rosenbaum(treated = rnorm(30, 0.5), control = rnorm(30))
#>    gamma      p_lower     p_upper
#> 1    1.0 3.017503e-03 0.003017503
#> 2    1.2 7.019737e-04 0.010150355
#> 3    1.4 1.633504e-04 0.024049382
#> 4    1.6 3.807120e-05 0.045718990
#> 5    1.8 8.890603e-06 0.074996526
#> 6    2.0 2.080443e-06 0.110911110
#> 7    2.2 4.877975e-07 0.152079033
#> 8    2.4 1.145850e-07 0.197004121
#> 9    2.6 2.696241e-08 0.244265340
#> 10   2.8 6.354344e-09 0.292614821
#> 11   3.0 1.499705e-09 0.341015876