# 因果推理课程

**Repository Path**: titzp/Introduction-to-Causal-Inference

## Basic Information

- **Project Name**: 因果推理课程
- **Description**: Introduction to Causal Inference
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

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- **Stars**: 0
- **Forks**: 2
- **Created**: 2022-05-07
- **Last Updated**: 2022-05-24

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## README

# 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](https://www.youtube.com/watch?v=CfzO4IEMVUk&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=1) [Slides](https://www.bradyneal.com/slides/1 - A Brief Introduction to Causal Inference.pdf) [Info](https://www.youtube.com/watch?v=xj-tzrm5Src&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=6) | Chapter 1 of ICI                | None                                                         |
| September 7  | Potential Outcomes A Complete Example with Estimation        | [Video](https://www.youtube.com/watch?v=q8x9aetyok0&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=8) [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)](https://www.nature.com/articles/ijo200882) |
| September 14 | Graphical Models                                             | [Video](https://www.youtube.com/watch?v=Go4EkHN_PcA&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=19) [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)](https://ftp.cs.ucla.edu/pub/stat_ser/r483-reprint.pdf) |
| September 21 | Backdoor Adjustment Structural Causal Models                 | [Video](https://www.youtube.com/watch?v=dB8r4Afmobo&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=28) [Slides](https://www.bradyneal.com/slides/4 - Causal Models.pdf) | Chapter 4 of ICI                | [Single World Intervention Graphs: A Primer (Richardson & Robins, 2013)](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.644.1881&rep=rep1&type=pdf) |
| September 28 | Randomized Experiments Frontdoor Adjustment *do*-calculus Graph-Based Identification | [Video](https://www.youtube.com/watch?v=z91LnTDyhtI&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=37) [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)](https://causalai.net/r60.pdf) |
| October 5    | Estimation [Susan Athey](https://athey.people.stanford.edu/) Guest Talk - Estimating Heterogeneous Treatment Effects (Oct 8th at 3 - 4 pm EDT) | [Video](https://www.youtube.com/watch?v=YzcOYU-s2t4&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=42) [Slides](https://www.bradyneal.com/slides/6 - Estimation.pdf) [Guest Talk](https://www.youtube.com/watch?v=oZoizsX3bts&list=PLoazKTcS0RzZ1SUgeOgc6SWt51gfT80N0&index=7) | Chapter 7 of ICI                | [Adapting Neural Networks for the Estimation of Treatment Effects (Shi, Blei, Veitch, 2019)](https://arxiv.org/abs/1906.02120) |
| October 12   | Unobserved Confounding, Bounds, and Sensitivity Analysis     | [Video](https://www.youtube.com/watch?v=IXNMYqUsBBQ&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=47) [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)](https://arxiv.org/abs/2003.01747) |
| October 19   | Instrumental Variables                                       | [Video](https://www.youtube.com/watch?v=Mco16tUSA-U&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=53) [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)](http://proceedings.mlr.press/v70/hartford17a.html) |
| October 26   | Difference-in-Differences [Alberto Abadie](http://economics.mit.edu/faculty/abadie) Guest Talk - Synthetic Control (Oct 29th at 10 - 11 am EDT) | [Video](https://www.youtube.com/watch?v=tT8xLRS_cRQ&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=58) [Slides](https://www.bradyneal.com/slides/9 - Difference-in-Differences.pdf) [Guest Talk](https://www.youtube.com/watch?v=nKzNp-qpE-I&list=PLoazKTcS0RzZ1SUgeOgc6SWt51gfT80N0&index=11) | Chapter 10 of ICI               | [Regression Discontinuity Designs in Economics (Lee & Lemieux, 2010)](https://www.princeton.edu/~davidlee/wp/RDDEconomics.pdf) |
| November 2   | --- Break Week - No Lecture ---                              | None                                                         | Past Readings                   | None                                                         |
| November 9   | Causal Discovery from Observational Data [Jonas Peters](http://web.math.ku.dk/~peters/) Guest Talk (November 13 at 10 am EST) | [Video](https://www.youtube.com/watch?v=lVE-4deFe7c&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=62) [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)](https://www.nature.com/articles/s41467-019-10105-3) |
| November 16  | Causal Discovery from Interventions                          | [Video](https://www.youtube.com/watch?v=de2ODel8F1k&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=69) [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)](https://arxiv.org/abs/1705.10220) |
| November 23  | Transfer Learning Transportability                           | [Video](https://www.youtube.com/watch?v=JNq4oCV9C5k&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=77) [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)](https://arxiv.org/abs/2006.07433) |
| November 30  | [Yoshua Bengio](https://yoshuabengio.org/profile/) Guest Talk - Causal Representation Learning (Dec 1st at 1 - 2:30 pm EST) | [Guest Talk](https://www.youtube.com/watch?v=rKZJ0TJWvTk&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=80) [Slides](https://www.bradyneal.com/slides/Yoshua_Bengio_Guest_Talk_Towards_Causal_Representation_Learning.pdf) | None                            | [Invariant Risk Minimization (Arjovsky et al., 2019)](https://arxiv.org/abs/1907.02893) |
| December 7   | Counterfactuals Mediation                                    | [Video](https://www.youtube.com/watch?v=f8PEpthLlN4&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=81) [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)](https://ftp.cs.ucla.edu/pub/stat_ser/r321-ijcai05.pdf) |

# 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

上述教材你也可以在如下码云仓库进行查看

# 论文阅读清单

1. Motivation and Preview - No reading group

2. Potential Outcomes

   - [Does obesity shorten life? The importance of well-defined interventions to answer causal questions (Hernán & Taubman, 2008)](https://www.nature.com/articles/ijo200882)
   - [Does Obesity Shorten Life? Or is it the Soda? On Non-manipulable Causes (Pearl, 2018)](https://ftp.cs.ucla.edu/pub/stat_ser/r483-reprint.pdf)

3. Graphical Models and SCMs

   - [On the Interpretation of do(x) (Pearl, 2019)](https://www.degruyter.com/view/j/jci.2019.7.issue-1/jci-2019-2002/jci-2019-2002.xml)
   - [Quantifying causal influences (Janzing et al., 2012)](https://arxiv.org/abs/1203.6502)
   - [Trygve Haavelmo and the Emergence of Causal Calculus (Pearl, 2014)](https://ftp.cs.ucla.edu/pub/stat_ser/r391.pdf)

4. Randomized Experiments, Frontdoor Adjustment, and do calculus

   - [Single World Intervention Graphs: A Primer (Richardson & Robins, 2013)](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.644.1881&rep=rep1&type=pdf)

   - [The Paper of How: Estimating Treatment Effects Using the Front-Door Criterion (Bellemare & Bloem, 2019)](http://marcfbellemare.com/wordpress/wp-content/uploads/2019/08/BellemareBloemFDCAugust2019.pdf)
   - [On Pearl’s Hierarchy and the Foundations of Causal Inference (Bareinboim et al., 2020)](https://causalai.net/r60.pdf)

5. Estimation and Conditional Average Treatment Effects

   - [Estimating individual treatment effect: generalization bounds and algorithms (Shalit, Johansson, & Sontag, 2017)](https://arxiv.org/abs/1606.03976)
   - [Adapting Neural Networks for the Estimation of Treatment Effects (Shi, Blei, Veitch, 2019)](https://arxiv.org/abs/1906.02120)
   - [Generalized Random Forests (Athey, Tibshirani, Wager, 2019)](https://arxiv.org/abs/1610.01271)
   - [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning (Künzel et al., 2017)](https://arxiv.org/abs/1706.03461) (caution: not about meta-learning in the ML sense)

6. Sensitivity Analysis

   - [Making sense of sensitivity: extending omitted variable bias (Cinelli & Hazlett, 2019)](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssb.12348)
   - [Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding (Veitch & Zaveri, 2020)](https://arxiv.org/abs/2003.01747)
   - [An Introduction to Sensitivity Analysis for Unobserved Confounding in Non-Experimental Prevention Research (Liu, Kuramoto, & Stuart, 2013)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3800481/)
   - [Sensitivity Analysis of Linear Structural Causal Models (Cinelli et al., 2019)](http://proceedings.mlr.press/v97/cinelli19a.html)

7. Instrumental Variables, Regression Discontinuity, Difference-in-Differences, and Synthetic Control

   - [Improving Causal Inference: Strengths and Limitations of Natural Experiments (Dunning, 2007)](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.883.6034&rep=rep1&type=pdf)
   - [Alternative Causal Inference Methods in Population Health Research: Evaluating Tradeoffs and Triangulating Evidence (Mattay et al., 2019)](http://paa2019.populationassociation.org/uploads/190202)
   - [Deep IV: A Flexible Approach for Counterfactual Prediction (Hartford et al., 2017)](http://proceedings.mlr.press/v70/hartford17a/hartford17a.pdf)
   - [Regression Discontinuity Designs in Economics (Lee & Lemieux, 2010)](https://www.princeton.edu/~davidlee/wp/RDDEconomics.pdf)
   - Synthetic Controls (there are several different Abadie papers; message me, if you’re interested in this topic)

8. BREAK

9. Causal Discovery without Experiments

   - [Inferring causation from time series in Earth system sciences (Runge et al., 2019)](https://www.nature.com/articles/s41467-019-10105-3)
   - [Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (Mooij et al., 2016)](https://jmlr.org/papers/v17/14-518.html)

   - [Do-calculus when the True Graph Is Unknown (Hyttinen, Eberhardt, Jarvisalo, 2015)](https://www.cs.helsinki.fi/u/mjarvisa/papers/hyttinen-eberhardt-jarvisalo.uai15.pdf)
   - [Review of Causal Discovery Methods Based on Graphical Models (Glymour, Zhang, & Spirtes, 2019)](https://www.frontiersin.org/articles/10.3389/fgene.2019.00524/full)
   - [Causal inference by using invariant prediction: identification and confidence intervals (Peters, Bühlmann & Meinshausen, 2016)](https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12167)
   - [Nonlinear causal discovery with additive noise models (Hoyer et al., 2008)](https://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models.pdf)
   - [Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes (Huang et al., 2020)](https://arxiv.org/abs/1903.01672)

10. Causal Discovery with Experiments

    - [Experiment Selection for Causal Discovery (Hyttinen, Eberhardt, Hoyer, 2013)](https://jmlr.csail.mit.edu/papers/v14/hyttinen13a.html)
    - [Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Hauser & Bühlmann, 2012)](https://arxiv.org/abs/1104.2808)
    - [Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions (Yang, Katcoff, & Uhler, 2018)](https://arxiv.org/abs/1802.06310)
    - [Joint Causal Inference from Multiple Contexts (Mooij, Magliacane, & Claassen, 2020)](https://www.jmlr.org/papers/volume21/17-123/17-123.pdf)

11. Transportability and Transfer Learning

    - [External Validity: From Do-Calculus to Transportability Across Populations (Pearl & Bareinboim, 2014)](https://ftp.cs.ucla.edu/pub/stat_ser/r400-reprint.pdf)
    - [A causal framework for distribution generalization (Christiansen et al., 2020)](https://arxiv.org/abs/2006.07433)
    - [Causal inference and the data-fusion problem (Bareinboim & Pearl, 2016)](https://www.pnas.org/content/113/27/7345)
    - [On Causal and Anticausal Learning (Schölkopf et al., 2012)](https://icml.cc/2012/papers/625.pdf)
    - [Domain Adaptation under Target and Conditional Shift (Zhang et al., 2013)](http://proceedings.mlr.press/v28/zhang13d.html)
    - [Multi-Source Domain Adaptation: A Causal View (Zhang, Gong, & Schölkopf., 2015)](https://mingming-gong.github.io/papers/AAAI_MULTI.pdf)
    - [Invariant Models for Causal Transfer Learning (Rojas-Carulla et al., 2016)](http://www.jmlr.org/papers/volume19/16-432/16-432.pdf)
    - [Domain Adaptation As a Problem of Inference on Graphical Models (Zhang et al., 2020)](https://arxiv.org/abs/2002.03278)
    - [Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions (Magliacane et al., 2018)](https://arxiv.org/abs/1707.06422)

12. Counterfactuals, Mediation, and Path-Specific Effects

    - [Identification, Inference and Sensitivity Analysis for Causal Mediation Effects (Imai, Keele, & Yamamoto, 2010)](https://imai.fas.harvard.edu/research/files/mediation.pdf)
    - [Identifiability of Path-Specific Effects (Avin, Shpitser, & Pearl, 2005)](https://ftp.cs.ucla.edu/pub/stat_ser/r321-ijcai05.pdf)
    - [Interpretation and Identification of Causal Mediation (Pearl, 2014)](https://ftp.cs.ucla.edu/pub/stat_ser/r389.pdf)

13. TBD - Overflow Week

14. Causal Representation Learning

    - [Visual Causal Feature Learning (Chalupka, Perona, & Eberhardt, 2015)](http://www.its.caltech.edu/~fehardt/papers/CPE_UAI2015.pdf)
    - [Discovering causal signals in images (Lopez-Paz et al., 2017)](https://arxiv.org/abs/1605.08179)
    - [Invariant Risk Minimization (Arjovsky et al., 2019)](https://arxiv.org/abs/1907.02893)