greedy-ancestral-search 0.1.1

Official implementation of the Greedy Ancestral Seach (GAS) algorithm from the paper 'On the Number of Conditional Independence Tests in Constraint-based Causal Discovery'.

Greedy Ancestral Search

Official implementation of the Greedy Ancestral Seach (GAS) algorithm from the paper "On the Number of Conditional Independence Tests in Constraint-based Causal Discovery".

Installation

To get started, you'll need Python 3.10 or newer. You can install greedy-ancestral-search directly from PyPI:

pip install greedy-ancestral-search

Usage

Here is a simple example of using GAS with an oracle d-separation tester to learn the essential graph from a randomly generated directed acyclic graph.

import random
import networkx as nx
from greedy_ancestral_search import greedy_ancestral_search

# 1. Define the set of nodes.
number_of_nodes = 10
nodes = set(range(number_of_nodes))

# 2. Create a random ground-truth DAG to simulate data.
edge_probability = 0.5
G = nx.DiGraph()
G.add_nodes_from(nodes)
for u in nodes:
    for v in nodes:
        if u < v and random.random() < edge_probability:
            G.add_edge(u, v)

# 3. Define a conditional independence tester function.
#    This function queries the ground-truth graph for d-separation.
#    In a real-world scenario, this would be a statistical test on data.
def oracle_tester(X, Y, condition_set):
    """Returns True if X and Y are d-separated by condition_set in G."""
    return nx.is_d_separator(G, X, Y, condition_set)

# 4. Run the GAS algorithm.
#    The function takes the set of nodes and the CI tester as input.
undirected_edges, directed_edges = greedy_ancestral_search(nodes, oracle_tester)

# The algorithm returns the edges of the learned essential graph.
print("Undirected Edges:", undirected_edges)
print("Directed Edges:", directed_edges)

Development

First, clone the repository:

git clone --recurse-submodules https://github.com/uhlerlab/greedy-ancestral-search.git
cd greedy-ancestral-search

Dependencies

This project uses uv for dependency management. For more information, see the official documentation. If you don't have uv, install it with the official script:

curl -LsSf https://astral.sh/uv/install.sh | sh

Then, install all development and runtime dependencies:

uv sync --all-groups

Tests

The test suite validates the algorithm's correctness by generating random DAGs and verifying that the algorithm output, using an oracle d-separation tester, corresponds to the correct essential graph. Tests are located in the tests/ directory.

To run the standard test suite:

uv run pytest

To run the comprehensive test suite, which uses larger graphs and more iterations (as used in our CI workflow):

export CI=1 && uv run pytest

Benchmarks

The benchmarks/ directory contains scripts for performance evaluation. To run a specific benchmark:

uv run benchmarks/<script>.py

Available scripts include:

• benchmarks/airfoil.py
• benchmarks/neighborhood-comparison.py
• benchmarks/nodes-comparison.py
• benchmarks/sample-size.py
• benchmarks/scale-free-graphs.py
• benchmarks/sergio.py

Citation

If you use GAS in your work, please cite:

@misc{mones2026numberconditionalindependencetests,
      title={On the Number of Conditional Independence Tests in Constraint-based Causal Discovery}, 
      author={Franquesa Mon\'{e}s, Marc and Zhang, Jiaqi and Uhler, Caroline},
      year={2026},
      eprint={2603.21844},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2603.21844}, 
}