resources.md

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“Learn from yesterday, live for today, hope for tomorrow. The important thing is not to stop questioning.” — Albert Einstein

Books

• Pearl, Judea, and Dana Mackenzie. The Book of Why: The New Science of Cause and Effect. Basic Books, 2018.
• Peters, Jonas, Dominik Janzing, and Bernhard Schölkopf. Elements of causal inference: foundations and learning algorithms. MIT press, 2017
• Pearl, Judea. Causal inference in statistics: An overview. Statistics surveys 3 (2009): 96-146
• Spirtes, Peter, et al. Causation, prediction, and search. MIT press, 2000
• Mumford, Stephen, and Rani Lill Anjum. Causation: a very short introduction. OUP Oxford, 2013.
• Hernán MA, Robins JM. Causal Inference. Boca Raton: Chapman & Hall/CRC, forthcoming, 2018.
• Halpern, Joseph Y. Actual causality. MiT Press, 2016.
• Morgan, Stephen L., and Christopher Winship. Counterfactuals and causal inference. Cambridge University Press, 2015.
• Spirtes, Peter, et al. Causation, prediction, and search. MIT press, 2000.

Papers

• Schölkopf, Bernhard, et al. On causal and anticausal learning arXiv preprint arXiv:1206.6471 (2012)
• Aristotle on Causality
• Pearl, Judea. Theoretical impediments to machine learning with seven sparks from the causal revolution. arXiv preprint arXiv:1801.04016, (2018).
• Guo, Ruocheng, et al. A Survey of Learning Causality with Data: Problems and Methods. arXiv preprint arXiv:1809.09337, 2018.
• Angrist, Joshua D., Guido W. Imbens, and Donald B. Rubin. Identification of causal effects using instrumental variables. Journal of the American statistical Association 91.434 (1996): 444-455.
• Bottou, Léon, et al. Counterfactual reasoning and learning systems: The example of computational advertising. The Journal of Machine Learning Research 14.1 (2013): 3207-3260.
• Eckles, Dean, and Eytan Bakshy. Bias and high-dimensional adjustment in observational studies of peer effects. arXiv preprint arXiv:1706.04692 (2017).
• Olteanu, Alexandra, Onur Varol, and Emre Kiciman. Distilling the outcomes of personal experiences: A propensity-scored analysis of social media. ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, 2017.
• Shalizi, Cosma Rohilla, and Andrew C. Thomas. Homophily and contagion are genetically confounded in observational social network studies. Sociological methods & research 40.2 (2011): 211-239.
• Sharma, Amit, Jake M. Hofman, and Duncan J. Watts. Split-door criterion for causal identification: Automatic search for natural experiments. arXiv preprint arXiv:1611.09414 (2016).
• Wager, Stefan, and Susan Athey. Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association (2017).

Courses

• Causal Diagrams: Draw Your Assumptions Before Your Conclusions

  Causal diagrams have revolutionized the way in which researchers ask: Does X have a causal effect on Y? They have become a key tool for researchers who study the effects of treatments, exposures, and policies. By summarizing and communicating assumptions about the causal structure of a problem, causal diagrams have helped clarify apparent paradoxes, describe common biases, and identify adjustment variables. As a result, a sound understanding of causal diagrams is becoming increasingly important in many scientific disciplines.

  What you'll learn with this course:

  • How to translate expert knowledge into a causal diagram
  • How to draw causal diagrams under different assumptions
  • Using causal diagrams to identify common biases
  • Using causal diagrams to guide data analysis

• A Crash Course in Causality: Inferring Causal Effects from Observational Data

  At the end of the course, you will be able to:

  1. Define causal effects using potential outcomes
  2. Describe the difference between association and causation
  3. Express assumptions with causal graphs
  4. Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting)
  5. Identify which causal assumptions are necessary for each type of statistical method

• Center for Causal Discovery Summer Short Course 2016

  Overview of graphical models, loading Tetrad, Causal graphs and interventions

• Causal & Statistical Reasoning

  This course provides an introduction to causal and statistical reasoning. After taking this course, students will be better prepared to make rational decisions about their own lives and about matters of social policy. They will be able to assess critically—even if informally—claims that they encounter during discussions or when considering a news article or report. A variety of materials are presented, including Case Studies where students are given the opportunity to examine a causal claim, and the Causality Lab, a virtual environment to simulate the science of causal discovery. Students have frequent opportunities to check their understanding and practice their skills.

  This course is meant to serve students in several situations. One, it is meant for students who will only take one such research methods course, and are interested in gaining basic skills that will help them to think critically about claims they come across in their daily lives, such as through a news article. Two, it is meant for students who will take a few statistics courses in service of a related field of study. Three, it is meant for students interested in the foundations of quantitative causal models: called Bayes Networks.

• Tutorial on Causal Inference and Counterfactual Reasoning

  This tutorial will introduce participants to concepts in causal inference and counterfactual reasoning, drawing from a broad literature on the topic from statistics, social sciences and machine learning.

  Sections

  • Introduction: Patterns and predictions are not enough
  • Methods: Conditioning-based methods and natural experiments
  • Considerations: Special considerations with large-scale and network data
  • Broader Landscape: Heterogeneous treatment effects, machine learning and causal discovery
  • References: Further reading

• CIS600-Data-Science-Causal-Inference

Videos & Lectures

• Four Lectures on Causality

  • Part 1: We introduce structural causal models and formalize interventional distributions. We define causal effects and show how to compute them if the causal structure is known.
  • Part 2: We present three ideas that can be used to infer causal structure from data: (1) finding (conditional) independencies in the data, (2) restricting structural equation models and (3) exploiting the fact that causal models remain invariant in different environments.
  • Part 3: If time allows, we show how causal concepts could be used in more classical machine learning problems.

  • Lectures: 1, 2, 3, and 4

• The Mathematics of Causal Inference: with Reflections on Machine Learning

  The development of graphical models and the logic of counterfactuals have had a marked effect on the way scientists treat problems involving cause-effect relationships. Practical problems requiring causal information, which long were regarded as either metaphysical or unmanageable can now be solved using elementary mathematics. Moreover, problems that were thought to be purely statistical, are beginning to benefit from analyzing their causal roots.