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。

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

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)
• Does Obesity Shorten Life? Or is it the Soda? On Non-manipulable Causes (Pearl, 2018)

• 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)

• 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)

• 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)

• 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)

• 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)

• 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)

• 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)

• 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)

• 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)

• 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)