causalchange 0.1.1

CausalChange

causalchange provides implementations of some score-based algorithms for causal discovery, with focus on addressing different forms of distribution shifts.

Setup

Install the core package with pip install causalchange, and with additional dependencies for the resp. algorithms using pip install causalchange[spacetime] and pip install causalchange[cmm].

Quick Example

import numpy as np
import pandas as pd

from causalchange import Topic

rng = np.random.default_rng(0)
n = 300

# chain: X -> Y -> Z
x = rng.normal(size=n)
y = 2.0 * x + 0.2 * rng.normal(size=n)
z = -1.0 * y + 0.2 * rng.normal(size=n)

X = pd.DataFrame({"X": x, "Y": y, "Z": z})

cc = Topic(score_type="lin", seed=0)
cc.fit(X)

print("Topological order:", cc.topological_order_)
print("Edges:", sorted(cc.graph_.edges()))

Further Examples

See the notebooks/ for basic usage on small synthetic examples. The algorithms are

• TOPIC, for score-based causal DAG discovery from tabular data in topological order [1],
• LINC, for score-based causal discovery from multiple contexts, i.e., multiple tabular datasets under latent distribution shifts/interventions [2],
• CMM, for score-based causal discovery from latent mixtures, i.e., a tabular dataset comprised of different hidden populations/contexts/interventions [3].
• SpaceTime, for temporal causal discovery, changepoint detection, and causal clustering analysis in time series or multi-context time series [4].

Documentation

See the docs/ for additional documentation.

References

[1] Xu, S., Mameche, S., and Vreeken, J. Information-theoretic Causal Discovery in Topological Order. AISTATS, 2025.

[2] Mameche, S., Kaltenpoth, D., and Vreeken, J. Learning Causal Models under Independent Changes. NeurIPS, 2023.

[3] Mameche, S., Kalofolias, J., and Vreeken, J. Causal Mixture Models: Characterization and Discovery. NeurIPS, 2025.

[4] Mameche, S., Cornanguer, L., Ninad, U., and Vreeken, J. SpaceTime: Causal Discovery from Non-stationary Time Series. AAAI, 2025.