Papers
我们将会精心挑选10篇 overview 论文非常细致的阅读, 挑选若干重要论文复现.
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The Big Picture
The Big Picture
Overviews of causal inference. 收集描绘整个因果团队研究的格局的文章, 并且进行解读和分析.
Pearl(2019) The seven tools of causal inference, with reflections on machine learning
Pearl(2019) The seven tools of causal inference, with reflections on machine learning
The seven tools of causal inference, with reflections on machine learning (Pearl 2019) is, in my view, the most important paper for causal inference. We will see this paper through different angles:
Quick Start. 摘抄, 理解并且背诵论文中的经典观点, 措辞和表达.
数学硬核解读 我需要给出论文中涉及概念和数学定义和例子.
Christina(2018) Causal Structure Learning
Christina(2018) Causal Structure Learning
Causal discovery overview (Annu. Rev. Stat. Appl. 2018. 5:13.1–13.21)
(I) Quick Start
Hachem Saddiki(2019) A Primer on Causality in Data Science
Hachem Saddiki(2019) A Primer on Causality in Data Science
A Primer on Causality in Data Science.
1. Quick Start 整体把握 简单的抓取了论文中的主体内容, 进行了基本的解读和总结, 并且用一个例子展示了相关内容.
Ruocheng(2019) A Survey of Learning Causality with Data: Problems and Methods.
Ruocheng(2019) A Survey of Learning Causality with Data: Problems and Methods.
A Survey of Learning Causality with Data: Problems and Methods.
1. Quick Start 整体把握 进行了基本的解读和总结, 并且用例子展示相关内容.
（Vincent 2018）总结了 ACIC Data Challenge, 包括因果模型评估方法和优胜竞赛方案。
(I) 内容简单梳理, (II) Discussion 部分理解, (III) Motivation, IV) Data and Generative Models, (V) Causal Inference and Key Features, (VI) 因果推断模型评估
论文复现 Reproducibility
论文复现 Reproducibility
复现一些论文
Christos(2017) Causal Effect Inference with Deep Latent-Variable Models
Christos(2017) Causal Effect Inference with Deep Latent-Variable Models
Christos(2017) Causal Effect Inference with Deep Latent-Variable Models 使用深度生成模型来进行因果建模.
1. (I) Quick Start 整体把握 简单的抓取了论文中的主体内容, 进行了基本的解读和总结.
2. (II) (II) 网络结构 重点关注论文使用的网路及其代码实现。找到了相关的 github project.
Jianlin Su(2018) and Hu(2018)
Jianlin Su(2018) and Hu(2018)
Variational Inference: A Unified Framework of Generative Models and Some Revelations 提出了一种统一的框架来理解 VAE 和其他生成模型。这里给出这种理解下各种生成模型及其实现。Hu(2018) 是统一这种框架的更好的说明.
1. 苏剑林论文快速开始整体把握 理解他的博客和论文内容。
2. Hu(2018) On unifying deep generative models.
Recently Reading Important papers
Recently Reading Important papers
介绍一些最近在读的重要论文。see More Important Papers on Colab
2019-06-25(Mar2019)Causal inference from observational data: Estimating the effect of contributions on visitation frequency at LinkedIn 我们需要 causal inference with observational data for that A/B tests are usually costly. 这文章例子不错. (Christina2019)Conditional Variance Penalties and Domain Shift Robustness 一个好的模型应该 robust under shift interventions.2019-05-27 Perfect Match: A Simple Method for Learning Representations for Counterfactual Inference with Neural Networks2019-05-24 Transportability of Causal and Statistical Relations: A Formal Approach. 迁移学习基础性文章 learning note2019-05-23 What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems. Distinguishing causal from effect using boservational data: methods and benchmarks2019-05-9 A Second Chance to Get Causal Inference Right: A Classification of Data Science Tasks 因果推断前言综述 by Migual. A recent influx of data analysts, many not formally trained in statistical theory, bring a fresh attitude that does not a priori exclude causal questions. 2019-04-18 Causal deconvolution by algorithmic generative models (Nature Machine Intelligence volume 1, pages58–66 (2019)) 的解读Google Sites
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