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.

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.

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

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

Bayesian Causal Inference in Doubly Gaussian DAG-probit Models

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