Structural Causal Model Python Packages

y0

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

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.

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

Deterministic selective-labels harness: measures PD models on the declined population real data never sees, and prices it in profit. Plants ground truth; the operating frontier is a 25-seed distribution, not a point. sklearn-only, byte-reproducible.

bel2scm

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