Structural Causal Model Python Packages

causal-worlds

Generate fictional-but-coherent causal operations worlds (executable sim + time-series + ground-truth causal answer-key) from a natural-language description — for benchmarking causal-discovery and control agents.

y0

❓y0 (pronounced "why not?") is for causal inference in Python

actualcauses

Code for the python model `actualcauses` that implements algorithms for HP-causes identification.

esa-2scm

ESA-2SCM for Causal Discovery: Causal Modeling with Elastic Segmentation-based Synthetic Instrumental Variable

closed-loop-default-detection

Fidelity-gated synthetic SCM that stress-tests probability-of-default modeling under selective labels and reports the model's honest operating frontier. The do() oracle real lending data can't be. Grades g-computation vs naive conditioning against planted ground truth. sklearn-only; 66 tests; fully deterministic.

bel2scm

Generate dynamic structural causal models from biological knowledge graphs encoded in the Biological Expression Language (BEL)