Test for balance for inverse-probability-weighted estimators
Comparison of model-adjusted covariate distributions across treatment groups
Kernel density plots
Kernel density plot comparing propensity scores across treatment groups
Treatment-effects models extract experimental-style causal effects from observational data.
In experimental data, treatment groups must be assigned randomly, meaning characteristics across groups will be approximately equal.
Treatment-effects estimators reweight the observational data in hopes of achieving experimental-like balanced data results.
If the reweighting is successful, then the weighted distribution of each covariate should be the same across treatment groups. In such cases, we say that the treatment model "balanced" the covariates.
We can examine whether the treatment model balanced the covariates and perform a statistical test.
Three diagnostics and one test are provided.
One diagnostic reports, for each covariate, the model-adjusted difference in means in the treatment groups and the ratio of variances.
Another diagnostic graphs the model-adjusted estimated pdfs of covariates; these pdfs can be examined visually to verify that they are approximately equal.
The third graphical diagnostic is the same as the second but uses box plots rather than smoothed pdfs.
The statistical test is an overidentification test. It tests whether the model-adjusted means of the covariates are the same between groups.