Stat 263/363: Experimental Design

Overview

Correlation is not causality. You've probably heard that before in any number of regression classes. If you want to infer causality from data, then the best way is to use randomized experiments. Maybe it is the only way.

In experimental design we look at how to choose the data that we will gather. In addition to being able to make causal conclusions, we also look at how to maximize the statistical efficiency of the generated data set.

Experimental design as a subject is about 100 years old. The methods in this course date back to agricultural field trials. Since then the ideas have seen use in medicine, manufacturing, quality control, computer aided design and electronic commerce. Each new field takes the previous methods and then starts adapting them. Possibly the first clinical trial was that of James Lind in 1747 showing that citrus is effective against scurvy. (It was not immediately adopted.)

There will be about 5 problem sets and a project.

Classes

History corner of the quad: 200-303 Mon & Wed 2:15 to 3:30

Instructor

Art Owen

Sequoia Hall 130

My userid is owenpelican on stanfordpelican.edu (remember to remove the waterfowl)

Office: Friday 2:15

TA

• Yunjin Choi & Wed 11 to 1, Sequoia Hall 242
        Sequoia Hall 208     yunjinpenguin@stanfordpenguin.edu

Delete any and all Antarctic birds from the TA's email

Texts

Our main text is "Statistics for Experimenters" by Box, Hunter and Hunter.
There is another good book by Montgomery, but it is very expensive and versions change quickly, making it a poor choice for classroom use.

Supplementary readings

• Paul Holland's article on Neyman-Rubin causality.
• Kohavi, Henne and Sommerfield's article on A/B tests for e-commerce.
• Agrawal and Goyal's article on Thompson sampling
• Bubeck and Cesa-Bianchi's monograph on bandit problems.
• Wikipedia entry on Hadamard matrices for Plackett-Burmann designs
• S. Georgiou's review of supersaturated designs.
• Krahmen and Ward's review of design for compressed sensing

Problems

Problems

I expect to send a small number of important emails about problem sets and the homework there. Most other announcements will be made in class. If you email me about the class, be sure to have stat 363 or stat 263 in your subject line. Otherwise, your email won't show when I search for course related emails.

We will count days late on each problem set. Each day late is penalized by 10% of the homework value. Homework more than 3 days late will ordinarily get 0. If you're travelling, you can email a pdf file. For sickness, interviews and other events, up to 3 late days total are forgiven at the end of the quarter. (Work late enough to get zero does not get redeemed though.)