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
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Prompt 1: "Compute the bounds on the estimand for a causal query."
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Prompt 2: "I have a Python script that I want to run with Autolab, can you help me?"
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Prompt 3: "I have a dataset in CSV format, how can I analyze it using Autolab?"
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Prompt 4: "What are the steps to create a causal problem with Autolab?"
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Prompt 5: "How can I set the causal estimand for my problem in Autolab?"
Features and commands
| Feature/Command | Description |
|---|
autolab | This 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. |
For AI
Nameautolab_causality
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().
Updates
First added6 November 2023