do-calculus

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.

Causing: CAUsal INterpretation using Graphs

Machine learning research code for causal perception: comparing competing structural causal models (SCMs) via interventional and counterfactual distributions, applied to fair credit decisions. Open source by Santander AI Lab.

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

Summary of useful results in Causal Inference

"Causality: Models, Reasoning, and Inference-Judea Pearl(2009)"中文翻译及学习笔记

Use regression, inverse probability weighting, and matching to close confounding backdoors and find causation in observational data

GenPark AI Agent Skill - Structural Causal Model (SCM) DAG engine evaluating observational distributions and simulating Pearl's do-calculus interventions.

GenPark AI Agent Skill - Structural Causal Model (SCM) DAG engine evaluating observational distributions and simulating Pearl's do-calculus interventions.

GenPark AI Agent Skill - Graph-theoretic backdoor criterion validator and minimal confounder adjustment set identifier ensuring unconfounded causal effect estimation.

GenPark AI Agent Skill - Propensity score matching and Inverse Probability Weighting (IPW) estimator calculating Average Treatment Effect (ATE) under conditional ignorability.

GenPark AI Agent Skill - Pearl's 3-step counterfactual inference engine executing Abduction, Action (do-surgery), and Prediction to answer what-if causal inquiries.

GenPark AI Agent Skill - Graph-theoretic backdoor criterion validator and minimal confounder adjustment set identifier ensuring unconfounded causal effect estimation.

GenPark AI Agent Skill - Propensity score matching and Inverse Probability Weighting (IPW) estimator calculating Average Treatment Effect (ATE) under conditional ignorability.

GenPark AI Agent Skill - Instrumental Variable (IV) Two-Stage Least Squares (2SLS) causal estimator isolating unobserved confounding and testing instrument strength.

GenPark AI Agent Skill - Instrumental Variable (IV) Two-Stage Least Squares (2SLS) causal estimator isolating unobserved confounding and testing instrument strength.

GenPark AI Agent Skill - Pearl's 3-step counterfactual inference engine executing Abduction, Action (do-surgery), and Prediction to answer what-if causal inquiries.

A minimal causal world model — learn causal graphs, intervene, plan.

A Powerful Python Library for Causal Inference

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.