User Guide#

Use these guides when you want workflow-oriented documentation before diving into the API reference. Each page focuses on a concrete task and links to the relevant examples and public APIs.

Core Workflow#

Causal Discovery and Structure Learning

Learn causal graphs from data with constraint-based, score-based, and expert-guided algorithms.

Causal Discovery and Structure Learning
Parameter Estimation

Learn CPDs from data using MLE, Bayesian priors, or EM for missing data.

Parameter Estimation
Probabilistic Inference

Compute posteriors, marginals, and MAP assignments with exact or approximate methods.

Probabilistic Inference
Simulations

Sample observational, interventional, and conditional data from fitted models.

Simulations

Causal Inference#

Causal Identification

Determine identifiability using backdoor adjustment and frontdoor criteria.

Causal Identification
Causal Estimation

Estimate treatment effects and interventional distributions from causal graphs and data.

Causal Estimation

Evaluation & Data#

Metrics

Evaluate learned graphs with supervised and unsupervised metrics.

Metrics
Example Datasets

Curated benchmark datasets with ground-truth graphs and expert knowledge.

Example Datasets
Example Models

Ready-made networks from bnlearn, bnrep, and dagitty for benchmarking and exploration.

Example Models

Utilities#

Exporting / Importing Models

Save and load models in BIF, NET, XMLBIF, XDSL, and other formats.

Exporting / Importing Models
Defining a Custom Model

Define graphs and CPDs directly for discrete, continuous, or dynamic models.

Defining a Custom Model
Plotting Models

Visualize model structure with Graphviz, daft, and networkx backends.

Plotting Models

Contributing#

Extensibility

Add new algorithms, datasets, and metrics using the built-in extension templates.

Extensibility