diff --git a/doubleml/rdd/datasets/simple_dgp.py b/doubleml/rdd/datasets/simple_dgp.py
index ff59b94da..aa386c985 100644
--- a/doubleml/rdd/datasets/simple_dgp.py
+++ b/doubleml/rdd/datasets/simple_dgp.py
@@ -34,7 +34,9 @@ def make_simple_rdd_data(n_obs=5000, p=4, fuzzy=True, binary_outcome=False, **kw
         dim_x : int
             The number of independent covariates. Default is 3.
         a : float
-            Factor to control interaction of score and covariates to the outcome equation.
+            Factor to control interaction of score and covariates to the outcome equation. Default is 0.0.
+        tau : float
+            Parameter to control the true effect in the generated data at the given cutoff. Default is 1.0.
 
     Returns
     -------
@@ -48,7 +50,8 @@ def make_simple_rdd_data(n_obs=5000, p=4, fuzzy=True, binary_outcome=False, **kw
 
     cutoff = kwargs.get('cutoff', 0.0)
     dim_x = kwargs.get('dim_x', 3)
-    a = kwargs.get('a', 0)
+    a = kwargs.get('a', 0.0)
+    tau = kwargs.get('tau', 1.0)
 
     score = np.random.normal(size=n_obs)
     # independent covariates
@@ -62,7 +65,7 @@ def make_simple_rdd_data(n_obs=5000, p=4, fuzzy=True, binary_outcome=False, **kw
     g_cov = np.sum(covs, axis=1)
 
     g0 = 0.1 * score**2
-    g1 = 1 + 0.1 * score**2 - 0.5 * score**2 + a * np.sum(X, axis=1) * score
+    g1 = tau + 0.1 * score**2 - 0.5 * score**2 + a * np.sum(X, axis=1) * score
 
     eps_scale = 0.2
     # potential outcomes with independent errors
diff --git a/doubleml/rdd/rdd.py b/doubleml/rdd/rdd.py
index 23ff97630..0ea051fb9 100644
--- a/doubleml/rdd/rdd.py
+++ b/doubleml/rdd/rdd.py
@@ -166,7 +166,16 @@ def __str__(self):
 
                 lines.append(line)
             result = "\n".join(lines)
-            return result
+
+            additional_info = (
+                "\nDesign Type:        " + ("Fuzzy" if self.fuzzy else "Sharp") +
+                f"\nCutoff:             {self.cutoff}" +
+                f"\nFirst Stage Kernel: {self.fs_kernel}" +
+                f"\nFinal Bandwidth:    {self.h}"
+            )
+
+            return result + additional_info
+
         else:
             return "DoubleML RDFlex Object. Run `.fit()` for estimation."