diff --git a/examples/01_data_preparation_for_training.ipynb b/examples/01_data_preparation_for_training.ipynb
index d42c8f7..e74876d 100644
--- a/examples/01_data_preparation_for_training.ipynb
+++ b/examples/01_data_preparation_for_training.ipynb
@@ -423,7 +423,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv_dev_gpu",
+   "display_name": ".venv_dev",
    "language": "python",
    "name": "python3"
   },
diff --git a/pyproject.toml b/pyproject.toml
index 00aa66f..3f929f9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,7 +15,7 @@ build-backend = "setuptools.build_meta" # Standard entry point for setuptools bu
 
 [project]
 name = "twinweaver"
-version = "0.3.1"
+version = "0.3.3"
 description = "Converting longitudinal patient data into text for LLM-based event prediction and forecasting."
 
 # --- NEW/UPDATED FIELDS ---
diff --git a/tests/test_converter.py b/tests/test_converter.py
index d5d713a..a19d1c0 100644
--- a/tests/test_converter.py
+++ b/tests/test_converter.py
@@ -248,6 +248,64 @@ def test_event_categories_to_exclude_empty_list(setup_components):
     assert "drug pemetrexed is administered" in instruction
 
 
+def test_event_categories_excluded_from_input_but_kept_in_targets(mock_config, sample_data):
+    """Exclusion config must affect only input history, not forecasting targets."""
+    df_events, df_constant, df_constant_desc = sample_data
+
+    mock_config.split_event_category = "lot"
+    # Forecast drug events so target/answer is expected to include drug content
+    mock_config.event_category_forecast = ["drug"]
+    mock_config.event_category_events_prediction_with_naming = {"death": "death", "progression": "next progression"}
+    mock_config.constant_columns_to_use = ["birthyear", "gender", "histology", "smoking_history"]
+    mock_config.constant_birthdate_column = "birthyear"
+    mock_config.event_categories_to_exclude_from_input = ["drug"]
+
+    dm = DataManager(config=mock_config)
+    dm.load_indication_data(df_events, df_constant, df_constant_desc)
+    dm.process_indication_data()
+    dm.setup_unique_mapping_of_events()
+    dm.setup_hold_out_sets(validation_split=0.1, test_split=0.1)
+    dm.infer_var_types()
+
+    splitter_forecast = DataSplitterForecasting(
+        data_manager=dm,
+        config=mock_config,
+        max_forecasted_trajectory_length=pd.Timedelta(days=90),
+        max_split_length_after_split_event=pd.Timedelta(days=90),
+    )
+    splitter_forecast.setup_statistics()
+
+    data_splitter = DataSplitter(data_splitter_forecasting=splitter_forecast)
+
+    converter = ConverterInstruction(
+        nr_tokens_budget_total=4096, config=mock_config, dm=dm, variable_stats=splitter_forecast.variable_stats
+    )
+
+    patient_data = dm.get_patient_data("p0")
+    f_splits, e_splits, _ = data_splitter.get_splits_from_patient_with_target(patient_data)
+
+    assert e_splits is None
+    assert f_splits is not None and len(f_splits) > 0
+
+    result = converter.forward_conversion(
+        forecasting_splits=f_splits[0],
+        event_splits=None,
+        override_mode_to_select_forecasting="forecasting",
+    )
+
+    instruction = result["instruction"]
+    answer = result["answer"]
+
+    # Input context should not include excluded drug history lines
+    input_context = instruction.split("Task 1 is", 1)[0]
+    assert "drug pemetrexed is administered" not in input_context
+
+    # Target/answer must still contain the forecasted drug values
+    assert "Task 1 is forecasting:" in answer
+    assert "drug " in answer.lower()
+    assert "is administered" in answer.lower()
+
+
 def test_forward_conversion_inference(setup_components):
     """Test conversion for inference (no target string)."""
     dm, data_splitter, converter = setup_components
diff --git a/twinweaver/common/converter_base.py b/twinweaver/common/converter_base.py
index 3f76c07..35dd983 100644
--- a/twinweaver/common/converter_base.py
+++ b/twinweaver/common/converter_base.py
@@ -264,7 +264,7 @@ def _get_constant_string(self, constant: pd.DataFrame, constant_description: pd.
 
         return constant_string
 
-    def _preprocess_events(self, events: pd.DataFrame) -> pd.DataFrame:
+    def _preprocess_events(self, events: pd.DataFrame, is_input: bool = True) -> pd.DataFrame:
         """
         Performs initial preprocessing on the time-series event data.
 
@@ -299,11 +299,11 @@ def _preprocess_events(self, events: pd.DataFrame) -> pd.DataFrame:
         )
 
         # Exclude specified event categories from the input if configured
-        if self.config.event_categories_to_exclude_from_input:
-            events = events[
-                ~events[self.config.event_category_col].isin(self.config.event_categories_to_exclude_from_input)
-            ]
-
+        if is_input:
+            if self.config.event_categories_to_exclude_from_input:
+                events = events[
+                    ~events[self.config.event_category_col].isin(self.config.event_categories_to_exclude_from_input)
+                ]
         return events
 
     def _get_event_string(
diff --git a/twinweaver/instruction/converter_forecasting.py b/twinweaver/instruction/converter_forecasting.py
index b93e49a..e94dd38 100644
--- a/twinweaver/instruction/converter_forecasting.py
+++ b/twinweaver/instruction/converter_forecasting.py
@@ -127,7 +127,7 @@ def _generate_target_string(self, patient_split: DataSplitterForecastingOption)
 
         #: preprocess:
         target_data = patient_split.target_events_after_split.copy()
-        target_cleaned = self._preprocess_events(target_data.copy())
+        target_cleaned = self._preprocess_events(target_data.copy(), is_input=False)
 
         #: get delta between split and first target
         target_first_day = target_cleaned[self.config.date_col].min()