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E-value for unmeasured confounding (continuous-ATE scale)

Source: R/effects.R
e_value.Rd

Native implementation of the VanderWeele & Ding (2017) OLS E-value. Supply an outcome SD via sd_y for the recommended standardised workflow; when sd_y is left at its default of 1, it back to the closed-form continuous-scale RR proxy used by the Python port so both ports stay numerically aligned.

Usage

e_value(ate, se, null = 0, sd_y = 1)

Arguments

ate

Point estimate of the treatment effect.

se

Standard error of the ATE (must be > 0).

null

Null value. Default 0.

sd_y

Outcome standard deviation. Default 1 (use the closed-form proxy). Pass the empirical sd to route through EValue::evalues.OLS() when installed.

Value

Scalar E-value (>= 1).

Examples

e_value(ate = 0.5, se = 0.1)
#> [1] 2.529142