diff --git a/doubleml/did/datasets/dgp_did_CS2021.py b/doubleml/did/datasets/dgp_did_CS2021.py
index 1756d085..dae11483 100644
--- a/doubleml/did/datasets/dgp_did_CS2021.py
+++ b/doubleml/did/datasets/dgp_did_CS2021.py
@@ -313,16 +313,15 @@ def make_did_CS2021(n_obs=1000, dgp_type=1, include_never_treated=True, time_typ
     if anticipation_periods > 0:
         # filter time periods
         df = df[df["t"] >= time_periods[anticipation_periods]]
-        # filter treatment after anticipation periods
-        df = df[(df["d"] <= time_periods[-(anticipation_periods + 1)]) | pd.isna(df["d"])]
+        # filter treatment after anticipation periods (never treated: NaT for datetime, inf for float)
+        never_treated = pd.isna(df["d"]) if time_type == "datetime" else np.isinf(df["d"])
+        df = df[(df["d"] <= time_periods[-(anticipation_periods + 1)]) | never_treated]
 
         # update time periods by subtracting time delta
         if time_type == "datetime":
-            df = df[(df["d"] <= time_periods[-(anticipation_periods + 1)]) | pd.isna(df["d"])]
             df["t"] = df["t"].apply(lambda x: x - pd.DateOffset(months=anticipation_periods))
         else:
             assert time_type == "float"
-            df = df[(df["d"] <= time_periods[-(anticipation_periods + 1)]) | np.isinf(df["d"])]
             df["t"] = df["t"] - anticipation_periods
 
     return df
diff --git a/doubleml/did/tests/test_datasets.py b/doubleml/did/tests/test_datasets.py
index 8e079e9a..3198a8ba 100644
--- a/doubleml/did/tests/test_datasets.py
+++ b/doubleml/did/tests/test_datasets.py
@@ -72,6 +72,21 @@ def test_make_did_CS2021_return_types(dgp_type, include_never_treated, time_type
     assert isinstance(df, pd.DataFrame)
 
 
+@pytest.mark.ci
+@pytest.mark.parametrize("anticipation_periods", [0, 1, 2])
+def test_make_did_CS2021_time_types_equivalent(anticipation_periods):
+    """Datetime and float time types simulate the same panel, including all never-treated units."""
+    np.random.seed(3141)
+    df_dt = make_did_CS2021(n_obs=200, time_type="datetime", anticipation_periods=anticipation_periods)
+    np.random.seed(3141)
+    df_fl = make_did_CS2021(n_obs=200, time_type="float", anticipation_periods=anticipation_periods)
+
+    pd.testing.assert_frame_equal(df_dt.drop(columns=["d", "t"]), df_fl.drop(columns=["d", "t"]))
+    never_treated = np.isinf(df_fl["d"])
+    np.testing.assert_array_equal(pd.isna(df_dt["d"]), never_treated)
+    assert never_treated.any()
+
+
 @pytest.mark.ci
 def test_make_did_CS2021_exceptions():
     msg = r"time_type must be one of \('datetime', 'float'\). Got 2."