Basic demonstration of causal effects for Pearl's do-calculus
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Updated
Jun 17, 2019 - Jupyter Notebook
Basic demonstration of causal effects for Pearl's do-calculus
Use regression, inverse probability weighting, and matching to close confounding backdoors and find causation in observational data
Summary of useful results in Causal Inference
Automatically determine whether a causal effect is identifiable
"Causality: Models, Reasoning, and Inference-Judea Pearl(2009)"中文翻译及学习笔记
A Python implementation of the do-calculus of Judea Pearl et al.
A Powerful Python Library for Causal Inference
This repository contains an implementation of BP-CDM introduced in "Data-Driven Decision Support for Business Processes: Causal Reasoning on Interventions".
Memo's research works.
Causing: CAUsal INterpretation using Graphs
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.
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