Covariate balance across instrument arms

Computes standardized mean differences (SMDs) of the model covariates between the two instrument arms, before and after weighting by the inverse of the estimated instrument propensity score. Well-balanced weighted covariates (conventionally, absolute SMD below 0.1) indicate that the propensity score model is doing its job.

Usage

balance(object, ...)

# S3 method for class 'drlate'
balance(object, detail = FALSE, ...)

Arguments

object

A fitted drlate() object (with keep_data = TRUE).

...

Currently unused.

detail

Logical. If TRUE, append the IPW-weighted arm means (mean_weighted_z1, mean_weighted_z0) and the unweighted and weighted variance ratios (vratio_unweighted, vratio_weighted, each \(s_1^2 / s_0^2\)), mirroring the Stata latebalance summarize report. Defaults to FALSE.

Value

A data frame with one row per covariate and columns variable, smd_unweighted, and smd_weighted; with detail = TRUE, the four additional columns described above.

Details

The covariate set is the union of the columns of the instrument, outcome, and treatment model matrices (the intercept is dropped). The SMD denominator is the unweighted pooled standard deviation \(\sqrt{(s_1^2 + s_0^2)/2}\) in both columns, so the two columns are directly comparable. Weighted arm means are Hájek means using the inverse-propensity weights implied by the fit (for estimand = "latt", the Z=0 arm uses the ATT odds weights \(p/(1-p)\), matching the estimator).

See also

plot.drlate() with type = "balance" for the love plot.