An impact-evaluation workflow

In baselinr: WWC-Aligned Baseline Equivalence Tables for Education Impact Evaluations

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

This vignette walks from a study's raw data to a baseline-equivalence report, using the bundled (simulated) tutoring dataset.

library(baselinr)
data(tutoring)
head(tutoring)

tutoring is a simulated quasi-experimental evaluation: 200 students who received a tutoring program (treat = 1) and 200 who did not (treat = 0), with baseline covariates and a post-program posttest.

Step 1: assess baseline equivalence — before looking at the outcome

The credibility of any later effect estimate rests on whether the two groups were comparable at baseline. We pass the baseline covariates explicitly — crucially not posttest, which is an outcome, not a baseline covariate.

baseline_covs <- c("pretest", "attendance", "age", "female", "frpl", "ell")

equiv <- baseline_equivalence(tutoring, treatment = "treat",
                              covariates = baseline_covs)

knitr::kable(equiv, digits = 3)

baselinr automatically uses Hedges' g for the continuous covariates (pretest, attendance, age) and the Cox index for the binary ones (female, frpl, ell).

Step 2: read the categories

Each covariate falls into one of three What Works Clearinghouse categories:

equiv[, c("covariate", "effect_size", "wwc_category")]

• satisfied — the groups are equivalent on this covariate; nothing more to do.
• satisfied_with_adjustment — equivalence holds only if you statistically adjust for this covariate in the impact model. This is a commitment, not a pass: those covariates must appear in the model.
• not_satisfied — this covariate cannot establish equivalence even with adjustment. It's a threat to the study's credibility that you have to confront, not bury.

Step 3: visualise

love_plot(equiv)

The dashed lines mark the 0.05 and 0.25 thresholds; points are coloured by category. The plot makes the at-risk covariates obvious at a glance.

Step 4: a report-ready table

For a written report or a Quarto/HTML document, gt_baseline() returns a formatted gt table:

gt_baseline(equiv)

What this does and doesn't tell you

baselinr reports the baseline equivalence picture. It does not fit the impact model for you. The next steps are yours: include the satisfied_with_adjustment covariates in the model, and decide how to handle (or report the limitation of) any not_satisfied covariate before you interpret the program's effect on posttest.

baselinr documentation built on July 8, 2026, 9:07 a.m.