flopsearch

flopsearch

Python package providing an implementation of the FLOP causal discovery algorithm for linear additive noise models.

Installation

flopsearch can be installed via pip:

pip install flopsearch

Citing FLOP

If you use FLOP in your scientific work, please cite this paper:

@article{embracing2025,
  author  = {Marcel Wien{\"o}bst and Leonard Henckel and Sebastian Weichwald},
  title   = {{Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning}},
  journal = {{arXiv preprint arXiv:2510.04970}},
  year    = {2025}
}

Example

A simple example run of the FLOP algorithm provided by flopsearch.

import flopsearch
import numpy as np
from scipy import linalg

p = 10
W = np.diag(np.ones(p - 1), 1)
X = np.random.randn(10000, p).dot(linalg.inv(np.eye(p) - W))
X_std = (X - np.mean(X, axis=0)) / np.std(X, axis=0)
flopsearch.flop(X_std, 2.0, restarts=50)

Input and Output

As input, FLOP takes the data matrix, the BIC penalty parameter (we recommend 2.0 as a default choice) and either a timeout (in seconds) or the number of ILS restarts to control how long the search runs.

The output of FLOP is a CPDAG encoded with an adjacency matrix whose entry in row i and column j is 1 in case of a directed edge from the i-th to the j-th variable and 2 in case of an undirected edge between those variables (in case of an undirected edge, the entry in row j and column i is also 2, that is each undirected edge induces two 2's in the matrix).