Short Description

Statistical evidence from randomized experiments versus observational studies. Applications of randomization, e.g., A/B testing for website optimization.

Learning Outcomes

By the end of the course, students are expected to be able to:

  1. Distinguish between experimentally-generated data and observational data, with particular reference to the strength of ensuing statistical conclusions.
  2. Fit and interpret regression models for observational data, with particular reference to adjustment for potential confounding variables.
  3. Apply the principle of “block what you can, randomize what you cannot” in designing an A/B testing experiment.
  4. Choose appropriately between fixed-effect and random-effect regression models.

Reference Material

  • O’Neil, Cathy and Schutt, Rachel. “Causality,” Ch. 11 of Doing Data Science: Straight Talk from the Frontline, O’Reilly Media, 2013.
  • Tang, Diane, et al. “Overlapping Experiment Infrastructure: More, Better, Faster Experimentation.” Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2010.

Instructor (2016-2017)

Note: information on this page is preliminary and subject to change.