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Autolab Causality

Calls Autolab to compute bounds on causal queries.

Autolab Causality plugin calls Autolab to compute bounds on causal queries. It uses a DAG (Directed Acyclic Graph) to define the causal structure and loads data from a CSV file. Users can add probability constraints and set the estimand using query strings. The plugin runs an optimization program using the 'ipopt' solver to obtain the results. It's a powerful tool for analyzing causal relationships and obtaining bounds on estimands. Get causal insights with Autolab Causality!

Learn how to use Autolab Causality effectively! Here are a few example prompts, tips, and the documentation of available commands.

Example prompts

  1. Prompt 1: "Compute the bounds on the estimand for a causal query."

  2. Prompt 2: "I have a Python script that I want to run with Autolab, can you help me?"

  3. Prompt 3: "I have a dataset in CSV format, how can I analyze it using Autolab?"

  4. Prompt 4: "What are the steps to create a causal problem with Autolab?"

  5. Prompt 5: "How can I set the causal estimand for my problem in Autolab?"

Features and commands

autolabThis command runs the given Python script with the given data available in an Autolab environment. It computes the bounds on the estimand for a causal query. You need to provide the Python script and the data in CSV format.


User authenticationNo user authentication
API documentation

For AI

DescriptionCalls Autolab to compute bounds on causal queries. An example script, passed by the parameter v, is import warnings warnings.simplefilter(action='ignore', category=FutureWarning) # Import necessary modules from Autobounds from autobounds.causalProblem import causalProblem from autobounds.DAG import DAG # Define the causal DAG def create_dag(): # Initialize a DAG object dag = DAG() # Define the causal structure using a string representation # Example: "Z -> X, X -> Y, U -> X, U -> Y" represents a graph with edges Z->X, X->Y, U->X, and U->Y # 'unob' specifies unobserved variables dag.from_structure("Z -> X, X -> Y, U -> X, U -> Y", unob="U") return dag # Define the causal problem def create_problem(dag, data_path): # Initialize a causalProblem object with the DAG problem = causalProblem(dag) # Load data from a CSV file problem.load_data(data_path) # Add probability constraints to the problem problem.add_prob_constraints() return problem # Define and set the causal estimand def set_estimand(problem, estimand_query): # Define the estimand using a query string # Example: 'X(Z=1)=0&X(Z=0)=1' represents individuals who do not take the treatment when exposed to the instrument and take it when not exposed problem.set_estimand(problem.query(estimand_query)) # Run the analysis and print results def run_analysis(problem): # Write the optimization program program = problem.write_program() # Run the optimization program using the 'ipopt' solver result = program.run_pyomo('ipopt', verbose=False) # Print the results print('Bounds on the estimand:', result) # Main function to execute the analysis def main(): # Define the path to the data CSV file data_path = 'data.csv' # Define the query for the estimand estimand_query = 'X(Z=1)=0&X(Z=0)=1' # Create the DAG, causal problem, set the estimand, and run the analysis dag = create_dag() problem = create_problem(dag, data_path) set_estimand(problem, estimand_query) run_analysis(problem) # Execute the main function main().


First added6 November 2023

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