SCS Reads 2018-2019
Links to past episodes of SCS Reads
- SCS Reads 2017-2018 Counterfactuals and Causal Inference
- SCS Reads 2016-2017 Rethinking Statistics
- SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences
- SCS Reads 2014-2015
- Past and current nominations for SCS Reads
- SCS Reads -- past seminars
Converging to a plan this year
We will adopt a hybrid plan combining reading the Book of Why (first chapter to be discussed on October 5: PDF of first chapter) with other topics.
On September 21, Michael Friendly will present a talk on the history of the visualization of the Titanic data.
Candidates for 2018-2019
- Pearl and Mackenzie (2018) The Book of Why, in the fall term followed by the remaining chapters (8 to 12) of Morgan and Winship (2015) Counterfactuals and Causal Inference, 2nd ed. in the winter term.
- Kevin Hartnett (2018) To Build Truly Intelligent Machines, Teach Them Cause and Effect, Quanta Magazine: An interview with Judea Pearl on the Book of Why
- The Book of Why is not technically difficult and provides a broad overview including interesting historical details. We could cover this in one term. It is intended as a trade book so it's cheap and accessible. It's also very useful as a source of ideas for anyone who would like to include more causal ideas in lower level quantitative courses. Here's the review in the New York Times and a link to Amazon.
- Suggested by Georges
- Pros: Great synthesis including counterfactual and graphical approaches. Discusses related concepts and history. It covers, less formally, many of the ideas in the first portion of Morgan and Winship so it would allow new members to visit this material before reading the last 5 chapters of Morgan and Winship.
- Cons: We've just spent a year on causal models. Would we prefer to do something else?
Initial discussion of topics
A view was expressed that we should consider this seminar series more broadly, with a view to:
- wider, and more active participation by the seminar members
- topics or book chapters that would:
- enlist a volunteer as discussion leader, or organize the topic
- perhaps involve some concrete, practical examples used to illustrate the topic
Without prejudice to the choice of The Book of Why on causal inference, some suggested topics mentioned are listed below, though the general view was that, if we took this route, only 3-4 should be considered for this year. I'm just listing these, but they deserve to be fleshed out more for us to consider how & whether they would work.
- Reproducibility of research: How should statistical practice be informed by current controversies about the replication crisis and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. [MF: I should add some references here.]
- Roger Peng, Reproducible Research in Computational Science, Science, Vol. 334, Issue 6060, pp. 1226-1227 
- RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter
- Big Data problems: Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]
- Some statistical methods topics, not yet clearly articulated:
- Clustering methods [RC: Could this fall under the Big Data label?]
- Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]
- Consulting issues: Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]
- Survey Sampling: Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada.
- Evidence-based medicine: Ideas and implications
- Machine Learning, AI, Deep Learning: An Overview
- Disseminating technical information to non-technical audiences: in consulting and in teaching. [Could this be put together with 'consulting issues'?]
- Missing Data
- Statistical Paradoxes and Fallacies [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]
- Initial attempt at a creating a list: Paradoxes, Fallacies and Other Surprises. Please add, modify or comment.
Talk: Michael Friendly, 100+ Years of Titanic Graphs
- Slides: File:SCS-TitanicGraphs-2x2.pdf
- Chapters 2 and 3 of the Book of Why
- Chapters 4 and 5 of the Book of Why
- We plan to continue a discussion of Chapter 7 of the Book of Why focusing on details of the do-calculus.
- An interesting reference might be this 2013 article by Felix Elwert on Graphical Causal Models.
- We continue with Chapter 8 of The Book of Why, Counterfactuals: Mining Worlds That Could Have Been
- MF offered some examples of papers describing R packages for learning/doing causal modeling
- Causal Inference in Statistics: A Companion for R Users. This is designed to accompany the book Causal Inference in Statistics: A Primer by Pearl, Glymour and Jewell.
- Vignette for the CausalImpact package. This package implements a Bayesian approach to causal impact estimation in time series data. It is designed for a specific situation, when you have time series pre- and post- some intervention. I mention it here only because it produces nice graphs of
- A Recipe for inferference: Start with Causal Inference. Add Interference. Mix Well with R. by Bradley Saul & Michael Hudgens