Hi there! My name is Randall Lewis and I think economics is about to be hit by something big, namely big data.

I am an economic research scientist at Netflix. I focus on statistical measurement using econometrics and causal statistics to learn valuable insights from large data sets. I work on a variety of research fields ranging from the econometrics of social networks to large scale field experiments. I have primarily focused my skills on measuring the impact of online display and search advertising on important business outcomes such as clicks, page views, searches, survey outcomes, and both online and offline (in-store) sales.

Here is a link to my CV.

Contact Information:
Randall Lewis
randall [AT] econinformatics.com

LinkedIn: Exogenous Variation
Facebook: Exogenous Variation

Formal Biographical Sketch:

Randall Lewis is an Economic Research Scientist in the Science & Algorithms team at Netflix. As a “big data” econometrician, he combines machine learning and econometrics to develop scalable causal measurement and prediction systems that help humans and machine-learning algorithms make optimal choices.

Prior to joining Netflix in 2015, he worked at Google and Yahoo! Research where he studied advertising’s impact on human behavior and sought ways to improve the health and efficiency of digital markets. Randall attended MIT as a Presidential Fellow where he earned his PhD in economics. Earlier, he attended BYU as a Hinckley Presidential Scholar and graduated as a valedictorian with a double major in economics and mathematics.

While at Yahoo! Research from 2008-2012, his research focused on using both natural and controlled experiments to measure the causal effect of advertising on online purchases, in-store purchases, and new-customer acquisition. He contributed key insights about improving industry-standard methods for measuring advertising effectiveness, which often unwittingly confuse causation with mere correlation. He made key discoveries about the importance of ad frequency of impressions on consumer behavior, geographic patterns, and competitive externalities in advertising effectiveness.

His work using “Big Data” at Yahoo has encouraged him to tackle problems involving empirical economic research at scale. Large-scale data can be powerful at carefully measuring statistically small but economically valuable quantities. The diffuse effects of advertising provides examples of this style of work, known under the name of “econinformatics.” The economic, conceptual, and technical skills required by this new class of problems begs a new subdiscipline: tech economics. Randall’s efforts include promoting the diffusion and development of this expertise within the field of economics.

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