diff --git a/doubleml/tests/test_set_sample_splitting.py b/doubleml/tests/test_set_sample_splitting.py
index 0995d831..fa0a4394 100644
--- a/doubleml/tests/test_set_sample_splitting.py
+++ b/doubleml/tests/test_set_sample_splitting.py
@@ -236,3 +236,43 @@ def test_doubleml_set_sample_splitting_invalid_sets():
     msg = r"Invalid sample split. Test indices must be in \[0, n_obs\)."
     with pytest.raises(ValueError, match=msg):
         dml_plr.set_sample_splitting(smpls)
+
+
+@pytest.mark.ci
+def test_doubleml_set_sample_splitting_shuffled_indices():
+    """Test that externally provided partitions work with shuffled (unsorted) indices."""
+    # Create valid 2-fold partition with sorted indices
+    sorted_smpls = [([0, 1, 2, 3, 4], [5, 6, 7, 8, 9]), ([5, 6, 7, 8, 9], [0, 1, 2, 3, 4])]
+
+    # Create the same partition but with shuffled indices
+    shuffled_smpls = [([4, 1, 0, 3, 2], [8, 5, 9, 6, 7]), ([7, 9, 5, 6, 8], [2, 4, 0, 1, 3])]
+
+    # Both should work and produce equivalent results
+    dml_plr_sorted = DoubleMLPLR(dml_data, ml_l, ml_m, n_folds=2, n_rep=2, draw_sample_splitting=False)
+    dml_plr_shuffled = DoubleMLPLR(dml_data, ml_l, ml_m, n_folds=2, n_rep=2, draw_sample_splitting=False)
+
+    dml_plr_sorted.set_sample_splitting(sorted_smpls)
+    dml_plr_shuffled.set_sample_splitting(shuffled_smpls)
+
+    # Both should have same fold structure
+    assert dml_plr_sorted.n_folds == 2
+    assert dml_plr_shuffled.n_folds == 2
+    assert dml_plr_sorted.n_rep == 1
+    assert dml_plr_shuffled.n_rep == 1
+
+    # Fit both models
+    dml_plr_sorted.fit(store_predictions=True)
+    dml_plr_shuffled.fit(store_predictions=True)
+
+    # Check if coefficient estimates are identical
+    np.testing.assert_allclose(dml_plr_sorted.coef, dml_plr_shuffled.coef, rtol=1e-10)
+    np.testing.assert_allclose(dml_plr_sorted.se, dml_plr_shuffled.se, rtol=1e-10)
+
+    sorted_preds_l = dml_plr_sorted.predictions["ml_l"][:, 0, 0]  # First rep, first treatment
+    sorted_preds_m = dml_plr_sorted.predictions["ml_m"][:, 0, 0]
+    shuffled_preds_l = dml_plr_shuffled.predictions["ml_l"][:, 0, 0]
+    shuffled_preds_m = dml_plr_shuffled.predictions["ml_m"][:, 0, 0]
+
+    # Since predictions are stored by observation index, they should be identical
+    np.testing.assert_allclose(sorted_preds_l, shuffled_preds_l, rtol=1e-10)
+    np.testing.assert_allclose(sorted_preds_m, shuffled_preds_m, rtol=1e-10)