All Projects → xieliaing → CausalInferenceIntro

xieliaing / CausalInferenceIntro

Licence: MIT license
Causal Inference for the Brave and True的中文翻译版。全部代码基于Python,适用于计量经济学、量化社会学、策略评估等领域。英文版原作者:Matheus Facure

Programming Languages

Jupyter Notebook
11667 projects

Projects that are alternatives of or similar to CausalInferenceIntro

SyntheticControlMethods
A Python package for causal inference using Synthetic Controls
Stars: ✭ 90 (-56.52%)
Mutual labels:  econometrics, policy-evaluation, causal-inference
causeinfer
Machine learning based causal inference/uplift in Python
Stars: ✭ 45 (-78.26%)
Mutual labels:  econometrics, causal-inference
doubleml-for-py
DoubleML - Double Machine Learning in Python
Stars: ✭ 129 (-37.68%)
Mutual labels:  econometrics, causal-inference
doubleml-for-r
DoubleML - Double Machine Learning in R
Stars: ✭ 58 (-71.98%)
Mutual labels:  econometrics, causal-inference
tlverse-handbook
🎯 📕 Targeted Learning in R: A Causal Data Science Handbook
Stars: ✭ 50 (-75.85%)
Mutual labels:  causal-inference
awesome-quant-papers
This repository hosts my reading notes for academic papers.
Stars: ✭ 28 (-86.47%)
Mutual labels:  econometrics
hdfe
No description or website provided.
Stars: ✭ 22 (-89.37%)
Mutual labels:  econometrics
SMC.jl
Sequential Monte Carlo algorithm for approximation of posterior distributions.
Stars: ✭ 53 (-74.4%)
Mutual labels:  econometrics
perfect match
➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.
Stars: ✭ 100 (-51.69%)
Mutual labels:  causal-inference
armagarch
ARMA-GARCH
Stars: ✭ 59 (-71.5%)
Mutual labels:  econometrics
cfml tools
My collection of causal inference algorithms built on top of accessible, simple, out-of-the-box ML methods, aimed at being explainable and useful in the business context
Stars: ✭ 24 (-88.41%)
Mutual labels:  causal-inference
Probability Theory
A quick introduction to all most important concepts of Probability Theory, only freshman level of mathematics needed as prerequisite.
Stars: ✭ 25 (-87.92%)
Mutual labels:  econometrics
cfvqa
[CVPR 2021] Counterfactual VQA: A Cause-Effect Look at Language Bias
Stars: ✭ 96 (-53.62%)
Mutual labels:  causal-inference
Robyn
Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) package from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression with cross validation, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define m…
Stars: ✭ 433 (+109.18%)
Mutual labels:  econometrics
hayashir
R Companion to the textbook "Econometrics" by Fumio Hayashi
Stars: ✭ 29 (-85.99%)
Mutual labels:  econometrics
ScPoEconometrics
Undergraduate textbook for Econometrics with R
Stars: ✭ 100 (-51.69%)
Mutual labels:  econometrics
Microeconometrics.jl
Microeconometric estimation in Julia
Stars: ✭ 30 (-85.51%)
Mutual labels:  econometrics
rode
Rode facilitates Automated Governance in your software supply chain. This repository contains the rode API which is the primary interface between the rode UI or rode Collectors and metadata storage in Grafeas. The rode API provides functions for metadata search and storage as well as policy creation and evaluation.
Stars: ✭ 48 (-76.81%)
Mutual labels:  policy-evaluation
Statsmodels
Statsmodels: statistical modeling and econometrics in Python
Stars: ✭ 6,935 (+3250.24%)
Mutual labels:  econometrics
Financial Models Numerical Methods
Collection of notebooks about quantitative finance, with interactive python code.
Stars: ✭ 3,534 (+1607.25%)
Mutual labels:  econometrics

因果推断:从概念到实践

img

本系列是 Causal Inference for the Brave and True 这本书 DOI的中文翻译版,由巴西Nubank的Staff Data Scientist Matheus Facure 所著。该书用平实的语言和严谨的数学,以及实用的Python代码,结合经济学与社会学的策略评估和敏感性分析应用,对因果推断最新的概念、理论及实践进行了非常全面的介绍,既适合初学者入门,同时也适合技术管理专家回顾相关领域的整体知识。该书英文原版的Jupyter Notebooks可以由该Github地址获取。

本书主要基础是计量经济学,吸收了非常多学者,包括 Joshua Angrist, Alberto AbadieChristopher WaltersMiguel HernanJamie Robins 等,在这方面的最新研究,主要参考了以下资料:

这里非常感谢Matheus Facure同意我翻译该书的中文译本。中文翻译版会在两个地方同步发布:

  1. 方便国际读者的 Github地址
  2. 方便国内读者的 Gitee地址

第一部分中文翻译版的进度按照如下时间线开展:

章节 名称 出稿日期
第一章: 因果关系入门   2021-11-07 
第二章: 随机实验   2021-11-13 
第三章: 统计学回顾:最危险的公式    2021-11-25 
第四章: 图因果模型    2021-11-18 
第五章: 线性回归超常的有效性   2021-12-05 
第六章: 分组和虚拟变量   2021-12-12 
第七章: 混淆变量之外   2021-12-19 
第八章: 工具变量   2021-12-26 
第九章: 不服从与LATE效应   2021-12-30
10  第十章: 匹配   2022-01-10 
11  第十一章: 倾向性打分    2022-01-22
12  第十二章: 双稳健估计    2022-01-29 
13  第十三章: 面板数据和固定效应    2022-2-13 
14  第十四章: 双重差分    2022-02-20 
15  第十五章: 合成控制    2022-02-27 
16  第十六章: 断点回归设计    2022-03-06
17  第十七章: 预测模型    2022-03-19
18  第十八章: 异质干预效应与个性化    2022-03-26
19  第十九章: 评估因果模型    2022-08-31
20  第二十章: 即插即用的估计量    2022-10-10
21  第二十一章: 元学习器    2022-11-15

该书遵守MIT License

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].