py-scm

A Python library for exact associational, interventional, and counterfactual reasoning in Gaussian Bayesian Belief Networks (BBNs) and linear-Gaussian structural causal models.

py-scm works from:

• a directed acyclic graph

• per-variable means

• a covariance matrix

The current reasoning surface is linear-Gaussian:

• pquery() performs conditional multivariate-normal inference

• iquery() computes exact post-intervention moments under do(...)

• cquery() performs exact abduction-action-prediction when the factual world is fully observed

These methods return pandas objects by default. When pandas wrapping is not needed, set pandas=False to get raw NumPy-backed results with the same semantics.

These query types are regression-tested against closed-form linear-Gaussian oracles and benchmarked against R bnlearn on non-trivial Gaussian networks.

For a license, please send an email to info@rocketvector.io.

Contents

• Quickstart
  • Creating a model
  • Associational query
  • Interventional query
  • Counterfactual
  • Data sampling
  • Serde
• Benchmarking Against R bnlearn
• Bibliography

API Documentation

• py-scm
  • Common
  • Reasoning
  • Associational Query
  • Interventional Query
  • Counterfactual Query
  • Sampling
  • Serde
  • Learn

Indices and tables

• Index

• Module Index

• Search Page

Copyright

Software

Copyright 2024 Rocket Vector

Art

Copyright 2020 Daytchia Vang

Citation

@misc{rocketvector_pyscm_2024,
title={PySCM},
url={https://pyscm.rocketvector.io},
author={Vang, Jee},
year={2024},
month={Mar}}