bn-fit-modify

What problem does it solve?

This Skill provides a comprehensive guide for recovering the structure of Bayesian Networks (DAGs) from data, learning their parameters, and performing causal interventions.

Core Features & Use Cases

• DAG Recovery: Learn the causal structure from observational data using robust algorithms.
• Parameter Learning: Fit model parameters for continuous (Linear Gaussian) and discrete data.
• Causal Interventions: Understand and implement the do-calculus for interventional distributions.
• Use Case: Analyze a dataset of patient health metrics to infer causal relationships between lifestyle factors and disease, then simulate the effect of a specific intervention (e.g., a new diet) on disease probability.

Quick Start

Use the bn-fit-modify skill to recover the DAG structure from the provided 'patient_data.csv' file and then learn its parameters.