Balance tables for oal objects

A method for cobalt::bal.tab(): assesses balance on the (original-scale) covariates of an oal() fit under the inverse-probability weights implied by the outcome-adaptive lasso propensity score, which is supplied as a distance measure.

Usage

# S3 method for class 'oal'
bal.tab(x, ...)

Arguments

x: An oal object.
...: Further arguments passed on to cobalt::bal.tab() (e.g., un = TRUE, thresholds = c(m = 0.1)).

Value

A bal.tab object; see cobalt::bal.tab().

Details

The call delegates to the default cobalt machinery as cobalt::bal.tab(<covariates>, treat = x$treat, weights = x$weights, s.d.denom = <by estimand>, distance = data.frame(ps = x$ps), ...), so all the usual cobalt arguments (un, stats, thresholds, ...) are available and display conventions are cobalt's own. The selection criterion inside oal() is the papers' wAMD instead, computed natively on the standardized covariates; see oal_wamd().

References

Shortreed SM, Ertefaie A (2017). Outcome-adaptive lasso: variable selection for causal inference. Biometrics, 73(4), 1111-1122. doi:10.1111/biom.12679

Examples

data("lalonde", package = "MatchIt")
fit <- oal(treat ~ age + educ + married + re74, data = lalonde,
           outcome = ~ re78)
cobalt::bal.tab(fit, un = TRUE)
#> Balance Measures
#>             Type Diff.Un Diff.Adj
#> ps      Distance  0.8366  -0.1688
#> age      Contin. -0.2419  -0.1062
#> educ     Contin.  0.0448   0.1938
#> married   Binary -0.3236   0.0340
#> re74     Contin. -0.5958   0.5336
#> 
#> Effective sample sizes
#>            Control Treated
#> Unadjusted  429.    185.  
#> Adjusted    409.07   50.16