最新因果推理课程在线学习，附课件、PPT、书籍和文献资料
最新因果推理课程在线学习，附课件、PPT、书籍和文献资料
因果推理导论
2020年秋季
你已经找到了在线因果推理课程页面。
课程主页
https://www.bradyneal.com/causal-inference-course#course-textbook
这门课程由 Yoshua Bengio 高徒 Brady Neal 主讲，主要讲述因果推理相关知识。尽管课程文本是从机器学习的角度编写的，但这门课程是为任何有必要的先决条件，谁对学习因果关系的基础感兴趣的人。我尽我最大的努力整合来自许多不同领域的见解，利用因果推理，如流行病学、经济学、政治学、机器学习等。
课程安排(初步)
关于幻灯片，请注意:它们目前不能很好地与Adobe Acrobat协同工作，尽管它们似乎可以与其他PDF查看器协同工作。
| Week | Topics | Lecture | Readings | Reading Group Paper |
|---|---|---|---|---|
| August 31 | Motivation Course Preview Course Information | Video [Slides](https://www.bradyneal.com/slides/1 - A Brief Introduction to Causal Inference.pdf) Info | Chapter 1 of ICI | None |
| September 7 | Potential Outcomes A Complete Example with Estimation | Video [Slides](https://www.bradyneal.com/slides/2 - Potential Outcomes.pdf) | 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](https://www.bradyneal.com/slides/3 - The Flow of Association and Causation in Graphs.pdf) | 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](https://www.bradyneal.com/slides/4 - Causal Models.pdf) | 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](https://www.bradyneal.com/slides/5 - Identification.pdf) | 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](https://www.bradyneal.com/slides/6 - Estimation.pdf) 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](https://www.bradyneal.com/slides/7 - Unobserved Confounding.pdf) | 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](https://www.bradyneal.com/slides/8 - Instrumental Variables.pdf) | 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](https://www.bradyneal.com/slides/9 - Difference-in-Differences.pdf) 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](https://www.bradyneal.com/slides/10 - Causal Discovery from Observational Data.pdf) | 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](https://www.bradyneal.com/slides/11 - Causal Discovery from Interventions.pdf) | Chapter 12 of ICI (Coming soon) | Permutation-based Causal Inference Algorithms with Interventions (Wang et al., 2017) |
| November 23 | Transfer Learning Transportability | Video [Slides](https://www.bradyneal.com/slides/12 - Transfer Learning and Transportability.pdf) | 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](https://www.bradyneal.com/slides/14 - Counterfactuals and Mediation.pdf) | Chapter 14 of ICI (Coming soon) | Identifiability of Path-Specific Effects (Avin, Shpitser, & Pearl, 2005) |
视频地址：
https://www.youtube.com/watch?v=CfzO4IEMVUk&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=1
课程教材
该课程的配套教材选用了 Brady Neal 编写的 《Introduction to Causal Inference》。需要说明的是，前10章草稿(在整个课程中不断更新新的章节):
教材地址：https://www.bradyneal.com/Introduction_to_Causal_Inference-Aug27_2020-Neal.pdf
论文阅读清单
Motivation and Preview - No reading group
Potential Outcomes
Does obesity shorten life? The importance of well-defined interventions to answer causal questions (Hernán & Taubman, 2008) Does Obesity Shorten Life? Or is it the Soda? On Non-manipulable Causes (Pearl, 2018)
Graphical Models and SCMs
On the Interpretation of do(x) (Pearl, 2019) Quantifying causal influences (Janzing et al., 2012) Trygve Haavelmo and the Emergence of Causal Calculus (Pearl, 2014)
Randomized Experiments, Frontdoor Adjustment, and do calculus
Single World Intervention Graphs: A Primer (Richardson & Robins, 2013)
The Paper of How: Estimating Treatment Effects Using the Front-Door Criterion (Bellemare & Bloem, 2019)
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)
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (Mooij et al., 2016)
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
Identification, Inference and Sensitivity Analysis for Causal Mediation Effects (Imai, Keele, & Yamamoto, 2010) Identifiability of Path-Specific Effects (Avin, Shpitser, & Pearl, 2005) Interpretation and Identification of Causal Mediation (Pearl, 2014)
TBD - Overflow Week
Causal Representation Learning
Visual Causal Feature Learning (Chalupka, Perona, & Eberhardt, 2015) Discovering causal signals in images (Lopez-Paz et al., 2017) Invariant Risk Minimization (Arjovsky et al., 2019)