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

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

A Powerful Python Library for Causal Inference

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

Memo's research works.

This repository contains an implementation of BP-CDM introduced in "Data-Driven Decision Support for Business Processes: Causal Reasoning on Interventions".

Automatically determine whether a causal effect is identifiable

An open-source framework for measuring true marketing channel contributions using causal inference without user-level tracking.

🌱 Reconstruct genetic regulation timelines in _Arabidopsis thaliana_ using causal inference, addressing missing data and parameter selection challenges effectively.

Demystifying Judea Pearl's do-calculus and the Backdoor Criterion by hand. Resolving a curated Simpson's Paradox using structural causal modeling on superhero battle mechanics.

CausalLab — Interactive Causal Inference & Discovery Laboratory. 6 modules: SCM Builder, do-Calculus, PC Algorithm, Simpson's Paradox, IV Lab, Counterfactuals. Zero dependencies.

Causal reinforcement learning, organized around Bareinboim's 9-task taxonomy — confounded offline→online RL, POMIS, counterfactual policies, transportability, discovery, imitation, curricula, shaping, games. Honest about scope. pip install causalrl

Bayesian Causal Inference in Doubly Gaussian DAG-probit Models

Judea Pearl’s Causal Ladder, featuring Association, Intervention, and Counterfactual models.

Causal inference of post-transcriptional regulation timelines from long-read sequencing in Arabidopsis thaliana

Basic demonstration of causal effects for Pearl's do-calculus