Statistical evidence from randomized experiments versus observational studies. Applications of randomization, e.g., A/B testing for website optimization.
By the end of the course, students are expected to be able to:
- Distinguish between experimentally-generated data and observational data, with particular reference to the strength of ensuing statistical conclusions.
- Fit and interpret regression models for observational data, with particular reference to adjustment for potential confounding variables.
- Apply the principle of “block what you can, randomize what you cannot” in designing an A/B testing experiment.
- Choose appropriately between fixed-effect and random-effect regression models.
- 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.
Note: information on this page is preliminary and subject to change.