Website | @cdubhland
Chris Harland is a Data Scientist at Microsoft working on problems in Bing search, Windows, and MSN. He holds a PhD in Physics from the University of Oregon and has worked in a wide variety of fields spanning elementary science education, cutting edge biophysical research, and recommendation/personalization engines.
Ever since Chris started using Bayesian methods on a semi-regular basis the frequency with which he uses the phrase “well, maybe” in conversation with colleagues has increased ten fold. His colleagues have yet to forgive Bayes for this.
1:45pm–2:25pm Thursday, 10/16/2014
An increasingly common task for data science is the measurement and attribution of experimental impact. Using examples from healthcare.gov, Microsoft advertising, and Bing experimentation, we will explore the strengths, weaknesses, and pitfalls of techniques for dealing with impact and attribution in scenarios/data in which control experiments were not possible or otherwise not performed.
2:35pm–3:15pm Thursday, 10/16/2014
Location: Table B
If you’re stumped by a scenario that can’t adequately be answered by A/B testing, you need to talk to Chris. He’ll give you techniques for dealing with these “non-standard” quasi-experimental events, share some fun data stories, and chat with you about causal inference, experimentation, and tools of the trade.