Do Calculus Python Packages

dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

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.

causalrl

Causal reinforcement learning, organized around Bareinboim's 9-task taxonomy, confounded offline→online RL, POMIS, counterfactual policies, transportability, discovery, imitation, curricula.

causing

Causing: CAUsal INterpretation using Graphs

metadentify

Meta-/in-context/amortized causal inference for computational identifiability

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.

do-calculus

A Python implementation of the do-calculus of Judea Pearl et al.