因果机器学习课程 6.S091

# 6.S091: Causality ### Policy Evaluation, Structure Learning, and Representation Learning The official syllabus is at [https://github.com/csquires/6.S091-causality/blob/main/syllabus.pdf](https://github.com/csquires/6.S091-causality/blob/main/syllabus.pdf). Lecture notes will be posted on this page. Recordings are [here](https://www.youtube.com/channel/UC7ilO3m_TDzOXULn7xWV1RQ). ### Details **Instructor**: Chandler Squires \ **TA**: Katie Matton \ **Time**: Tuesday and Thursday, 1-3pm \ **Dates**: 01/10/23 - 02/02/23 \ **Location**: 4-231 \ **Credit**: 6 units ### Description In this course, we will cover introductory material from three active research areas related to causality and machine learning. In the first third of the course, we will discuss the fundamentals of policy evaluation, where a known causal structure is used to estimate causal quantities such as (conditional) average treatment effects. In this section, we will cover algorithms for identification of causal estimands, as well the principles behind state-of-the-art estimation methods based on double/de-biased machine learning. In the second third of the course, we will consider causal structure learning, i.e., the estimation of an unknown causal structure from data. We will cover classical algorithms such as the PC algorithm, as well as newer methods which incorporate interventional data and allow for unobserved confounding. We will also cover experimental design techniques for causal structure learning. In the final third of the course, we will discuss the emerging field of causal representation learning, highlighting recent papers which connect machine learning with more traditional causal principles. ### Schedule **Tuesday, Jan 10**: Introduction to Structural Causal Models ([lecture notes](lecture_notes/Lecture1.pdf), [recording](https://youtu.be/tOguq_esmk8)) \ **Thursday, Jan 12**: Policy Evaluation I: Identification ([lecture notes](lecture_notes/Lecture2.pdf), [recording](https://youtu.be/xFaKbeAKLMU)) \ **Tuesday, Jan 17**: Policy Evaluation II: Estimation ([lecture notes](lecture_notes/Lecture3.pdf)) \ **Thursday, Jan 19**: Causal Structure Learning I: Identifiability ([lecture notes](lecture_notes/Lecture4.pdf)) \ **Tuesday, Jan 24**: Causal Structure Learning II: The PC Algorithm and Greedy Algorithms ([lecture notes](lecture_notes/Lecture5.pdf)) \ **Tuesday, Jan 31**: Causal Structure Learning III: Experimental Design ([lecture notes](lecture_notes/Lecture6.pdf)) \ **Thursday, Feb 2**: Causal Representation Learning ([lecture notes](lecture_notes/Lecture7.pdf)) We will have study sessions on Wednesdays, 5:30-7:30, in 24-307. ### Problem Sets - Problem sets must be done in LaTeX - Printed problem sets must be turned in at the beginning of lecture. - Due dates: \ **Thursday, Jan 19**: PSet 1 due at 1pm EST \ **Thursday, Jan 26**: PSet 2 due at 1pm EST \ **Friday, Feb 3**: PSet 3 due at 11:59pm EST