摘要
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Machine Learning and Causal Inference Susan Athey, Stanford University and NBER About this author at RePEc
Summer Institute 2015 Methods Lectures: Machine Learning for Economists 暑期研究所2015年方法讲座: 经济学家的机器学习
Summer Institute 2015 Methods Lecture: Prediction, Classification, and Applications
Network Models 暑期学院2014方法讲座: 网络模型的理论与应用https://www.nber.org/people/matthew_jackson)
Summer Institute 2013 Methods Lecture: Applications: Using Text as Data 暑期学院2013年方法讲座: 应用: 使用文本作为数据
因果推断Causal Inference，中英文字幕—https://www.bradyneal.com/causal-inference-course
Introduction to Causal Inference
Fall 2020
You’ve found the online causal inference course page. Although, the course text is written from a machine learning perspective, this course is meant to be for anyone with the necessary prerequisites who is interested in learning the basics of causality. I do my best to integrate insights from the many different fields that utilize causal inference such as epidemiology, economics, political science, machine learning, etc. You can see the tentative course schedule below.
You can join the course Slack workspace where you can easily start discussions with other people who are interested in causal inference. For information about office hours, see the office hours section below. If you’re interested in leading a reading group discussion, check out the suggested reading group papers to see if one piques your interest. When emailing me about this course, please include “[Causal Course]” at the beginning of your email subject to help make sure I see your email. If you want to receive course updates, sign up for the course mailing list. The main textbook we’ll use for this course is Introduction to Causal Inference (ICI), which is a book draft that I’ll continually update throughout this course.
Course Schedule (tentative)
Note about slides: they currently don’t work well with Adobe Acrobat, though they seem to work with other PDF viewers.
| Week | Topics | Lecture | Readings | Reading Group Paper |
|---|---|---|---|---|
| August 31 | Motivation Course Preview Course Information | Video Slides Info | Chapter 1 of ICI | None |
| September 7 | Potential Outcomes A Complete Example with Estimation | Video Slides | Chapter 2 of ICI | Does obesity shorten life? The importance of well-defined interventions to answer causal questions (Hernán & Taubman, 2008) |
| September 14 | Graphical Models | Video Slides | Chapter 3 of ICI | Does Obesity Shorten Life? Or is it the Soda? On Non-manipulable Causes (Pearl, 2018) |
| September 21 | Backdoor Adjustment Structural Causal Models | Video Slides | Chapter 4 of ICI | Single World Intervention Graphs: A Primer (Richardson & Robins, 2013) |
| September 28 | Randomized Experiments Frontdoor Adjustment do-calculus Graph-Based Identification | Video Slides | Chapters 5-6 of ICI | On Pearl’s Hierarchy and the Foundations of Causal Inference (Bareinboim et al., 2020) |
| October 5 | Estimation Susan Athey Guest Talk - Estimating Heterogeneous Treatment Effects (Oct 8th at 3 - 4 pm EDT) | Video Slides Guest Talk | Chapter 7 of ICI | Adapting Neural Networks for the Estimation of Treatment Effects (Shi, Blei, Veitch, 2019) |
| October 12 | Unobserved Confounding, Bounds, and Sensitivity Analysis | Video Slides | Chapter 8 of ICI | Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding (Veitch & Zaveri, 2020) |
| October 19 | Instrumental Variables | Video Slides | Chapter 9 of ICI | Deep IV: A Flexible Approach for Counterfactual Prediction (Hartford et al., 2017) |
| October 26 | Difference-in-Differences Alberto Abadie Guest Talk - Synthetic Control (Oct 29th at 10 - 11 am EDT) | Video Slides Guest Talk | Chapter 10 of ICI | Regression Discontinuity Designs in Economics (Lee & Lemieux, 2010) |
| November 2 | —- Break Week - No Lecture —- | None | Past Readings | None |
| November 9 | Causal Discovery from Observational Data Jonas Peters Guest Talk (November 13 at 10 am EST) | Video Slides | Chapter 11 of ICI | Inferring causation from time series in Earth system sciences (Runge et al., 2019) |
| November 16 | Causal Discovery from Interventions | Video Slides | Chapter 12 of ICI (Coming soon) | Permutation-based Causal Inference Algorithms with Interventions (Wang et al., 2017) |
| November 23 | Transfer Learning Transportability | Video Slides | Chapter 13 of ICI (Coming soon) | A causal framework for distribution generalization (Christiansen et al., 2020) |
| November 30 | Yoshua Bengio Guest Talk - Causal Representation Learning (Dec 1st at 1 - 2:30 pm EST) | Guest Talk Slides | None | Invariant Risk Minimization (Arjovsky et al., 2019) |
| December 7 | Counterfactuals Mediation | Video Slides | Chapter 14 of ICI (Coming soon) | Identifiability of Path-Specific Effects (Avin, Shpitser, & Pearl, 2005) |
Potential Reading Group Papers by Week
We will have a small weekly reading group that runs in parallel to the course. Before any given week’s reading group meeting, 1-3 people will have read the week’s paper in detail and already thought about discussion topics. These 1-3 people will then lead a discussion of a small number of people who have all made themselves familiar with the paper. The discussion group will be kept small (at most 15) in order to facilitate quality discussion. You can ensure that you have a place in the discussion group every week you’d like by signing up to be a discussion leader for at least one week. Below, I give a list of potential reading group papers, organized by week/topic, just like the course schedule is. You can email me at bradyneal11@gmail.com to let me know that you’d like to lead a certain week’s discussion, which paper(s) you’re considering, or to discuss other papers you’d like to discuss that are not on the list.
Motivation and Preview - No reading group
Potential Outcomes
Graphical Models and SCMs
Randomized Experiments, Frontdoor Adjustment, and
do
-calculus
Single World Intervention Graphs: A Primer (Richardson & Robins, 2013)
- On Pearl’s Hierarchy and the Foundations of Causal Inference (Bareinboim et al., 2020)
Estimation and Conditional Average Treatment Effects
- Estimating individual treatment effect: generalization bounds and algorithms (Shalit, Johansson, & Sontag, 2017)
- Adapting Neural Networks for the Estimation of Treatment Effects (Shi, Blei, Veitch, 2019)
- Generalized Random Forests (Athey, Tibshirani, Wager, 2019)
- Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning (Künzel et al., 2017) (caution: not about meta-learning in the ML sense)
Sensitivity Analysis
- Making sense of sensitivity: extending omitted variable bias (Cinelli & Hazlett, 2019)
- Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding (Veitch & Zaveri, 2020)
- An Introduction to Sensitivity Analysis for Unobserved Confounding in Non-Experimental Prevention Research (Liu, Kuramoto, & Stuart, 2013)
- Sensitivity Analysis of Linear Structural Causal Models (Cinelli et al., 2019)
Instrumental Variables, Regression Discontinuity, Difference-in-Differences, and Synthetic Control
- Improving Causal Inference: Strengths and Limitations of Natural Experiments (Dunning, 2007)
- Alternative Causal Inference Methods in Population Health Research: Evaluating Tradeoffs and Triangulating Evidence (Mattay et al., 2019)
- Deep IV: A Flexible Approach for Counterfactual Prediction (Hartford et al., 2017)
- Regression Discontinuity Designs in Economics (Lee & Lemieux, 2010)
- Synthetic Controls (there are several different Abadie papers; message me, if you’re interested in this topic)
BREAK
Causal Discovery without Experiments
- Inferring causation from time series in Earth system sciences (Runge et al., 2019)
Do-calculus when the True Graph Is Unknown (Hyttinen, Eberhardt, Jarvisalo, 2015)
- Review of Causal Discovery Methods Based on Graphical Models (Glymour, Zhang, & Spirtes, 2019)
- Causal inference by using invariant prediction: identification and confidence intervals (Peters, Bühlmann & Meinshausen, 2016)
- Nonlinear causal discovery with additive noise models (Hoyer et al., 2008)
- Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes (Huang et al., 2020)
Causal Discovery with Experiments
- Experiment Selection for Causal Discovery (Hyttinen, Eberhardt, Hoyer, 2013)
- Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Hauser & Bühlmann, 2012)
- Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions (Yang, Katcoff, & Uhler, 2018)
- Joint Causal Inference from Multiple Contexts (Mooij, Magliacane, & Claassen, 2020)
Transportability and Transfer Learning
- External Validity: From Do-Calculus to Transportability Across Populations (Pearl & Bareinboim, 2014)
- A causal framework for distribution generalization (Christiansen et al., 2020)
- Causal inference and the data-fusion problem (Bareinboim & Pearl, 2016)
- On Causal and Anticausal Learning (Schölkopf et al., 2012)
- Domain Adaptation under Target and Conditional Shift (Zhang et al., 2013)
- Multi-Source Domain Adaptation: A Causal View (Zhang, Gong, & Schölkopf., 2015)
- Invariant Models for Causal Transfer Learning (Rojas-Carulla et al., 2016)
- Domain Adaptation As a Problem of Inference on Graphical Models (Zhang et al., 2020)
- Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions (Magliacane et al., 2018)
Counterfactuals, Mediation, and Path-Specific Effects
TBD - Overflow Week
Causal Representation Learning
Causal inference James M. Robins 因果推断学习分享
Causal Inference Book
Jamie Robins and I have written a book that provides a cohesive presentation of concepts of, and methods for, causal inference. Much of this material is currently scattered across journals in several disciplines or confined to technical articles. We expect that the book will be of interest to anyone interested in causal inference, e.g., epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, computer scientists… The book is divided in 3 parts of increasing difficulty: causal inference without models, causal inference with models, and causal inference from complex longitudinal data.
To cite the book, please use “Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.”
This book is only available online through this page. A print version (for purchase) is expected to become available some time in the future. The exact time, when known, will be announced here. The components of the book can be accessed by clicking on the links below:
- The Causal Inference book (1st edition, 2020)
- NHEFS data
- Computer code
Warning: At this stage, we may still revise and correct errors without documenting the changes. Please make sure you use the most updated version of the book posted here.
Summer Institute 2011 Methods Lecture: Dynare Exercise 夏季学院2011年方法讲座: 动态运动
Summer Institute 2011 Methods Lecture: Projection Methods
Summer Institute 2011 Methods Lecture: Models with Heterogeneous Agents
Summer Institute 2011 Methods Lecture: Perturbation Methods 夏季研究所2011年方法讲座: 摄动法
Summer Institute 2008 Methods Lecture: Econometrics of DSGE Models 2008年夏季研究所方法讲座: DSGE 模型的计量经济学
Summer Institute 2007 Methods Lecture: Linear Panel Data Models 夏季学院2007年方法讲座: 线性面板数据模型
Summer Institute 2007 Methods Lecture: Regression Discontinuity Designs 夏季研究所2007年方法讲座: 回归间断设计
Summer Institute 2007 Methods Lecture: Control Function and Related Methods 暑期学院2007方法讲座: 控制作用及相关方法
Summer Institute 2007 Methods Lecture: Bayesian Inference 2007年夏季学院方法讲座: 贝叶斯推断
Summer Institute 2007 Methods Lecture: Cluster and Stratified Sampling 2007年夏季研究所方法讲座: 集群和分层抽样
Summer Institute 2007 Methods Lecture: Partial Identification 暑期学院2007方法讲座: 部分鉴定
Summer Institute 2007 Methods Lecture: Difference-in-Differences Estimation 夏季学院2007年方法讲座: 差异中的差异估计
Summer Institute 2007 Methods Lecture: Discrete Choice Models 夏季学院2007年方法讲座: 离散选择模型
Summer Institute 2007 Methods Lecture: Missing Data 2007年夏季研究所方法讲座: 缺失数据
Summer Institute 2007 Methods Lecture: Weak Instruments and Many Instruments 暑期学院2007方法讲座: 弱仪器和多种仪器
Summer Institute 2007 Methods Lecture: Quantile Methods 夏季研究所2007方法讲座: 分位数方法
https://space.bilibili.com/522173499?spm_id_from=333.788.b_765f7570696e666f.1
