Books and Courses
Current:
Introduction to Causal Inference, Brady Neal
Pearl(2018)
- Causality:从科学到哲学
Causal Inference: The Mixtape
Judea Pearl 队友 Joseph Y. Halpern 关于因果理论的最新书籍
Collier 1999, 信息因果
前面的书籍大多数是因果理论本身的书籍，接下来是一些因果理论与大问题的书籍。
Algebraic Statistics by Seth Sullivant
Edge网站
Courses
Online courses relate to causal inference, including
Introduction to Causal Inference, Brady Neal
CS 228 - Probabilistic Graphical Models by Standford
https://github.com/altdeep/causalML by Robert Ness
The machine learning summer school (MLSS) series was started in 2002 with the motivation to promulgate modern methods of statistical machine learning and inference. It was motivated by the observation that while many students are keen to learn about machine learning, and an increasing number of researchers want to apply machine learning methods to their research problems, only few machine learning courses are taught at universities. Machine learning summer schools present topics which are at the core of modern Machine Learning, from fundamentals to state-of-the-art practice. The speakers are leading experts in their field who talk with enthusiasm about their subjects.
从符号到统计，再到 Causal AI, See More on Homepage
A Crash Course in Causality: Inferring Causal Effects from Observational Data
University of Pennsylvania
We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will
learn how causal effects are defined,
what assumptions about your data and models are necessary,
and how to implement and interpret some popular statistical methods.
Learners will have the opportunity to apply these methods to example data in R .
At the end of the course, learners should be able to:
1. Define causal effects using potential outcomes 2. Describe the difference between association and causation 3. Express assumptions with causal graphs 4. Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting) 5. Identify which causal assumptions are necessary for each type of statistical method.
Causal Diagrams: Draw Your Assumptions Before Your Conclusions
HarvardX: PH559x
Causal diagrams have revolutionized the way in which researchers ask: Does X have a causal effect on Y? They have become a key tool for researchers who study the effects of treatments, exposures, and policies. By summarizing and communicating assumptions about the causal structure of a problem, causal diagrams have helped clarify apparent paradoxes, describe common biases, and identify adjustment variables. As a result, a sound understanding of causal diagrams is becoming increasingly important in many scientific disciplines. By the end of the course you will be able:
To draw causal diagrams under different assumptions
To identify common biases using causal diagrams
To guide data analysis using causal diagrams
CS 228 - Probabilistic Graphical Models
Winter 2018-19
Website: https://cs228.stanford.edu
该课程提供了概率图模型基础内容。
该课程的最后一个部分变分自编码器有 with Bayesian Network 非常重要
Probabilistic graphical models are a powerful framework for representing complex domains using probability distributions, with numerous applications in machine learning, computer vision, natural language processing and computational biology. Graphical models bring together graph theory and probability theory, and provide a flexible framework for modeling large collections of random variables with complex interactions. This course will provide a comprehensive survey of the topic, introducing the key formalisms and main techniques used to construct them, make predictions, and support decision-making under uncertainty.
The aim of this course is to develop the knowledge and skills necessary to design, implement and apply these models to solve real problems. The course will cover:
(1) Bayesian networks, undirected graphical models and their temporal extensions;
(2) exact and approximate inference methods;
(3) estimation of the parameters and the structure of graphical models.
see Syllabus
This course offers a rigorous mathematical survey of causal inference at the Master’s level. by Michael E. Sobel, professor of Statistics at Columbia University.
There are 6 modules in this course(I), key ideas, randomization inference, regression, propensity score, matching and special topics. and 5 modules in the course(II), intro to mediation, more on mediation, IV stratification and regression discontinuity, logitudinal causal inference, interference and fixed effects.