diff --git a/tools/perf/probit_bart_microbenchmark.R b/tools/perf/probit_bart_microbenchmark.R
new file mode 100644
index 00000000..7d784a76
--- /dev/null
+++ b/tools/perf/probit_bart_microbenchmark.R
@@ -0,0 +1,41 @@
+library(microbenchmark)
+library(stochtree)
+
+# Generate data needed to train BART model
+n <- 1000
+p <- 20
+X <- matrix(runif(n * p), ncol = p)
+f_XW <- (((0 <= X[, 1]) & (0.25 > X[, 1])) *
+  (-7.5) +
+  ((0.25 <= X[, 1]) & (0.5 > X[, 1])) * (-2.5) +
+  ((0.5 <= X[, 1]) & (0.75 > X[, 1])) * (2.5) +
+  ((0.75 <= X[, 1]) & (1 > X[, 1])) * (7.5))
+z <- f_XW + rnorm(n, 0, 1)
+y <- z > 0
+test_set_pct <- 0.2
+n_test <- round(test_set_pct * n)
+n_train <- n - n_test
+test_inds <- sort(sample(1:n, n_test, replace = FALSE))
+train_inds <- (1:n)[!((1:n) %in% test_inds)]
+X_test <- X[test_inds, ]
+X_train <- X[train_inds, ]
+z_test <- z[test_inds]
+z_train <- z[train_inds]
+y_test <- y[test_inds]
+y_train <- y[train_inds]
+
+# Run microbenchmark
+bench_results <- microbenchmark(
+  bart(
+    X_train = X_train,
+    y_train = y_train,
+    X_test = X_test,
+    num_gfr = 10,
+    num_mcmc = 100,
+    general_params = list(
+      probit_outcome_model = T,
+      sample_sigma2_global = F
+    )
+  ),
+  times = 10
+)