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   4e-04   4e-04
#> 2    1.2   4e-04   4e-04
#> 3    1.4   4e-04   4e-04
#> 4    1.6   4e-04   4e-04
#> 5    1.8   4e-04   4e-04
#> 6    2.0   4e-04   4e-04
#> 7    2.2   4e-04   4e-04
#> 8    2.4   4e-04   4e-04
#> 9    2.6   4e-04   4e-04
#> 10   2.8   4e-04   4e-04
#> 11   3.0   4e-04   4e-04