The Seven Tools of Causal Inference with Reflections on Machine Learning 文章解读 - 代码天地

The Seven Tools of Causal Inference with Reflections on Machine Learning 文章解读

企业开发 2023-04-07 01:03:36 阅读次数: 0

目录

THE THREE LAYER CAUSAL HIERARCHY. 4

THE SEVEN TOOLS OF CAUSAL INFERENCE (OR WHAT YOU CAN DO WITH A CAUSAL MODEL THAT YOU COULD NOT DO WITHOUT?) 7

Tool 1: Encoding Causal Assumptions – Transparency and Testability. 10

Tool 2: Do-calculus and the control of confounding. 11

Tool 3: The Algorithmization of Counterfactuals. 12

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转载自blog.csdn.net/qq_46044325/article/details/128039111

The Seven Tools of Causal Inference with Reflections on Machine Learning 文章解读

Classification and inference with machine learning

Causal inference

人工智能的未来———因果推理：Causal Inference: What If chapter1 A DEFINITION OF CAUSAL EFFECT 文章解读

人工智能的未来———因果推理：Causal Inference: What If Chapter3 OBSERVATIONAL STUDIES 文章解读

人工智能的未来———因果推理：Causal Inference: What If chapter2 A Randomized experiments 文章解读

Causal Inference: Problem Set

【统计】Causal Inference

《【统计】Causal Inference》

因果推断（Causal Inference）概要

Causal Inference History, Perspectives, Adventures, and Unification 贝叶斯之父Pearl访谈解读

新书: The SAGE Handbook of Regression Analysis and Causal Inference

论文解析：Membership Inference Attacks Against Machine Learning Models（一看即懂）

论文笔记：Accurate Causal Inference on Discrete Data

2020年因果推断综述《A Survey on Causal Inference》

Causal Inference: What If (the book)模型的因果推理部分

基于反事实的因果推理Causal inference based on counterfactuals--一万六千字文献详细解读（因果关系的推理应用）【全文总结】

Reflections

核心！Machine Learning和 Deep Learning的深入解读

Machine Learning

Deep Learning - Machine Learning

Learning Path for Machine Learning

Machine Learning Mastery 博客文章翻译：深度学习与 Keras

科技新闻_每日一闻：Microsoft reveals DoWhy library for causal inference

一本好书：Causal Inference-Measuring the Effect of X on y

4月21日 PhD Debate预告 | 因果推理（Causal Inference）与时间序列（Time Series）

因果推断Causal Inference: What If (the book)没有模型的因果推理部分章节结构

腾讯开源分布式数据科学组件：Fast-Causal-Inference

seven

Inductive Representation Learning In Temporal Networks via Causal Anonymous Walks

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