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Fixed tutorial notebooks and added to test suite
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‎docs/source/tutorials/poisson_line_process/poisson_line_process_tutorial.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"id": "d7d27532-7995-4d76-b40e-e6ae9e7cc645",
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"metadata": {},
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"outputs": [
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"[1000 rows x 7 columns]"
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]
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},
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"execution_count": 3,
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 3,
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"id": "ac297d2d-5a2f-4c33-bbdc-967d54e24e3f",
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"metadata": {},
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"outputs": [],
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"from causal_testing.specification.variable import Input, Output\n",
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"from causal_testing.specification.causal_dag import CausalDAG\n",
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"from causal_testing.specification.scenario import Scenario\n",
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"from causal_testing.specification.causal_specification import CausalSpecification\n",
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"\n",
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"# Define the input variables \n",
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"\n",
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" num_shapes_abs,\n",
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" num_shapes_unit})\n",
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"\n",
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"causal_dag = CausalDAG(dag_file) # Secondly, create the Causal DAG \n",
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"\n",
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"# Finally, we instantiate the CausalSpecification and pass in the scenario and Causal DAG\n",
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"\n",
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"causal_specification = CausalSpecification(scenario, causal_dag) "
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"causal_dag = CausalDAG(dag_file) # Secondly, create the Causal DAG "
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 4,
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"id": "9b8491ab-0a90-4061-baee-8e1ecef7371d",
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"metadata": {},
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"outputs": [],
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"base_test_case = BaseTestCase(treatment_variable=intensity, outcome_variable=num_shapes_unit) # Create the base test case\n",
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"\n",
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"# Perform identification on the DAG using the base test case\n",
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"adjustment_set = causal_specification.causal_dag.identification(base_test_case) # Note: an empty adjustment set means there are no confounding variables that need to be controlled for"
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"adjustment_set = causal_dag.identification(base_test_case) # Note: an empty adjustment set means there are no confounding variables that need to be controlled for"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"id": "fa53a888-68e1-4f6f-babf-16d3a206ea49",
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"metadata": {},
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"outputs": [],
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" \"height\": test_results.estimator.treatment_value,\n",
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" \"control\": test_results.estimator.control_value,\n",
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" \"treatment\": test_results.estimator.treatment_value,\n",
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" \"risk_ratio\": test_results.test_value.value[0],\n",
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" \"risk_ratio\": test_results.effect_estimate.value[0],\n",
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" }]"
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]
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},
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 6,
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"id": "6bc8be40-bc95-4187-8771-4ce096acc7b5",
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"metadata": {},
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"outputs": [
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"3 8 16 8 16 3.699311"
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]
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},
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"execution_count": 7,
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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"cell_type": "markdown",
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"id": "e62216b2-60ed-49b7-a2eb-0ad755bd91fc",
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"metadata": {},
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"source": "From the above causal test results and the risk ratios, we can conclude that doubling the intensity does not cause the number of polygons per unit area to increase by a factor of 4 as we expected. Instead, it increases by factors ranging from 2.8 to 3.7, meaning that the metamorphic relation is violated at a 95% confidence level."
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"source": [
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"From the above causal test results and the risk ratios, we can conclude that doubling the intensity does not cause the number of polygons per unit area to increase by a factor of 4 as we expected. Instead, it increases by factors ranging from 2.8 to 3.7, meaning that the metamorphic relation is violated at a 95% confidence level."
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]
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},
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{
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"cell_type": "markdown",
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "67bf5061-720f-4b3a-a371-3ff3092e81e1",
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"metadata": {},
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"outputs": [],
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"\n",
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"base_test_case = BaseTestCase(treatment_variable=width, outcome_variable=num_shapes_unit) # Create the base test case\n",
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"\n",
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"adjustment_set = causal_specification.causal_dag.identification(base_test_case) # Calculate the adjustment set again (if it exists)\n",
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"adjustment_set = causal_dag.identification(base_test_case) # Calculate the adjustment set again (if it exists)\n",
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"\n",
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"for intensity in treatment_values:\n",
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" \n",
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" for width in control_values:\n",
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" for width_value in control_values:\n",
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" \n",
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" estimator = LinearRegressionEstimator(\n",
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" df=df, # Pass in the dataframe\n",
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" base_test_case = base_test_case, # Base test case we created above\n",
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" treatment_value = width + 1.0, # Changing the width\n",
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" control_value=float(width), # Baseline width values\n",
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" treatment_value = width_value + 1.0, # Changing the width\n",
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" control_value=float(width_value), # Baseline width values\n",
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" adjustment_set=adjustment_set, # Use the same adjustment set as list comprehension\n",
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" effect_modifiers={\"intensity\": intensity},\n",
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" formula=\"num_shapes_unit ~ width + I(intensity ** 2)+I(width ** -1)+intensity-1\", # Patsy formula describing a linear regression model\n",
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" base_test_case = base_test_case, # Pass in the base test case\n",
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" expected_causal_effect = Positive(), # We expect a positive increase\n",
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" estimate_type = \"ate_calculated\", # Calls the ate_calculated method in the linear regression estimator\n",
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" effect_modifier_configuration = {\"intensity\": intensity}, # Condition on (hold constant) the intensity value when calculating width\n",
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" estimator=estimator) # Pass in the estimator we created above\n",
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" \n",
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" test_results = causal_test_case.execute_test() # Execute the tests\n",
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" {\n",
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" \"control\": test_results.estimator.control_value,\n",
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" \"treatment\": test_results.estimator.treatment_value,\n",
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" \"intensity\": test_results.effect_modifier_configuration[\"intensity\"],\n",
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" \"ate\": test_results.test_value.value[0],\n",
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" \"ci_low\": test_results.confidence_intervals[0][0],\n",
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" \"ci_high\": test_results.confidence_intervals[1][0],\n",
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" \"intensity\": estimator.effect_modifiers[\"intensity\"],\n",
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" \"ate\": test_results.effect_estimate.value[0],\n",
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" \"ci_low\": test_results.effect_estimate.ci_low[0],\n",
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" \"ci_high\": test_results.effect_estimate.ci_high[0],\n",
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" }]\n"
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]
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 8,
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"id": "6c54392c-4e6b-42b3-b39a-e0d1d0ab25b7",
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"metadata": {},
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"outputs": [
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"9 1.0 2.0 2 -7.378642 -16.381136 1.623851"
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"metadata": {},
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"output_type": "execute_result"
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "Causal Testing Dev Env",
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"language": "python",
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"name": "python3"
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"name": "causal_testing_dev"
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"language_info": {
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"codemirror_mode": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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"version": "3.11.14"
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}
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"nbformat": 4,

‎docs/source/tutorials/vaccinating_elderly/vaccinating_elderly_tutorial.ipynb‎

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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"name": "stdout",
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"text": [
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"2025-10-31 13:49:34 - causal_testing.main - INFO - Setting up Causal Testing Framework...\n",
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"2025-10-31 13:49:34 - causal_testing.main - INFO - Loading DAG from dag.dot\n",
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"2025-10-31 13:49:34 - causal_testing.main - INFO - DAG loaded with 5 nodes and 3 edges\n",
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"2025-10-31 13:49:34 - causal_testing.main - INFO - Loading data from 1 source(s)\n",
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"2025-10-31 13:49:34 - causal_testing.main - INFO - Initial data shape: (60, 16)\n",
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"2025-10-31 13:49:34 - causal_testing.main - INFO - Setup completed successfully\n",
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"2025-10-31 13:49:34 - causal_testing.main - INFO - Loading test configurations from causal_tests.json\n",
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"2025-10-31 13:49:34 - root - INFO - Running tests in regular mode\n",
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"2025-10-31 13:49:34 - causal_testing.main - INFO - Running causal tests...\n",
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"100%|████████████████████████████████████████████| 9/9 [00:00<00:00, 356.92it/s]\n",
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"2025-10-31 13:49:34 - causal_testing.main - INFO - Saving results to causal_test_results.json\n",
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"2025-10-31 13:49:34 - causal_testing.main - INFO - Results saved successfully\n",
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"2025-10-31 13:49:34 - root - INFO - Causal testing completed successfully.\n"
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"Namespace(command='test', dag_path='dag.dot', output='causal_test_results.json', ignore_cycles=False, data_paths=['simulated_data.csv'], test_config='causal_tests.json', verbose=False, query=None, adequacy=False, bootstrap_size=None, silent=False, batch_size=0)\n",
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"2025-12-12 11:21:57 - causal_testing.main - INFO - Setting up Causal Testing Framework...\n",
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"2025-12-12 11:21:57 - causal_testing.main - INFO - Loading DAG from dag.dot\n",
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"2025-12-12 11:21:57 - causal_testing.main - INFO - DAG loaded with 5 nodes and 3 edges\n",
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"2025-12-12 11:21:57 - causal_testing.main - INFO - Loading data from 1 source(s)\n",
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"2025-12-12 11:21:57 - causal_testing.main - INFO - Initial data shape: (60, 16)\n",
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"2025-12-12 11:21:57 - causal_testing.main - INFO - Setup completed successfully\n",
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"2025-12-12 11:21:57 - causal_testing.main - INFO - Loading test configurations from causal_tests.json\n",
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"2025-12-12 11:21:57 - root - INFO - Running tests in regular mode\n",
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"2025-12-12 11:21:57 - causal_testing.main - INFO - Running causal tests...\n",
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"100%|████████████████████████████████████████████| 9/9 [00:00<00:00, 358.51it/s]\n",
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"2025-12-12 11:21:57 - causal_testing.main - INFO - Saving results to causal_test_results.json\n",
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"2025-12-12 11:21:57 - causal_testing.main - INFO - Results saved successfully\n",
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"2025-12-12 11:21:57 - root - INFO - Causal testing completed successfully.\n"
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]
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}
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"source": [
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"!python -m causal_testing test --data_paths simulated_data.csv --dag_path dag.dot --test_config causal_tests.json --output causal_test_results.json"
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"!python -m causal_testing test --data-paths simulated_data.csv --dag-path dag.dot --test-config causal_tests.json --output causal_test_results.json"
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"- [Documentation](https://causal-testing-framework.readthedocs.io/en/latest/index.html)\n",
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"- [Paper](https://dl.acm.org/doi/10.1145/3607184)"
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "80c1a475-cbee-4912-846b-584d252e90b0",
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"metadata": {},
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"outputs": [],
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"source": []
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"display_name": "Causal Testing Dev Env",
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"language": "python",
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"name": "causal_testing_dev"
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"language_info": {
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"name": "python",
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"nbconvert_exporter": "python",
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"version": "3.10.11"
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"version": "3.11.14"
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"nbformat": 4,

‎docs/source/tutorials/visualising_causal_test_results/visualise_causal_test_results.ipynb‎

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