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.

causing

Causing: CAUsal INterpretation using Graphs

causalrl

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

do-calculus

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

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.

metadentify

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