do-calculus
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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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Mar 14, 2025 - Python
Causing: CAUsal INterpretation using Graphs
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Dec 30, 2024 - Python
A Python implementation of the do-calculus of Judea Pearl et al.
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May 4, 2022 - Python
Summary of useful results in Causal Inference
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May 8, 2021 - TeX
Use regression, inverse probability weighting, and matching to close confounding backdoors and find causation in observational data
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Feb 27, 2020
"Causality: Models, Reasoning, and Inference-Judea Pearl(2009)"中文翻译及学习笔记
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Feb 18, 2022 - JavaScript
A Powerful Python Library for Causal Inference
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Sep 9, 2022 - Python
Memo's research works.
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Mar 7, 2025 - Jupyter Notebook
Automatically determine whether a causal effect is identifiable
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Aug 13, 2021 - Julia
This repository contains an implementation of BP-CDM introduced in "Data-Driven Decision Support for Business Processes: Causal Reasoning on Interventions".
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Nov 8, 2023 - Jupyter Notebook
Bayesian Causal Inference in Doubly Gaussian DAG-probit Models
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Aug 25, 2024 - R
Basic demonstration of causal effects for Pearl's do-calculus
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Jun 17, 2019 - Jupyter Notebook
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