Institute Industry Innovation

How the New Availability of Urban and Industrial Data are Impacting Our World from Public Safety to Jet Engines

Thursday, May 11, 2017 - 4:00pm to 5:30pm
Schapiro Hall (CEPSR) 750 (Costa Engineering Commons)
Columbia University
New York, NY 10027
United States

Columbia Data Science Institute Industry Innovation Seminars

(Talk 2 of 2)


Peter Marx, Vice President, Advanced Projects, GE Digital, Adjunct Professor, USC

Estimating Causal Effect of Ads in a Real-Time Bidding Platform

Wednesday, April 26, 2017 - 5:00pm to 6:00pm
Davis Auditorium | The Schapiro Center | Columbia University
530 West 120th Street
New York, NY 10027
United States

Columbia Data Science Innovation Seminars

Prasad Chalasani
SVP, Data Science
MediaMath

Title: Estimating Causal Effect of Ads in a Real-Time Bidding Platform

Abstract: A real-time bidding platform responds to incoming ad-opportunities ("bid requests") by deciding whether or not to submit a bid and how much to bid. If the submitted bid wins, the user is shown an ad. Advertisers hope that ad-exposure leads to an increased likelihood of a desired action, such as a click or conversion (purchase, etc). So an important quantity that advertisers want to measure is the causal effect of advertising, namely, what is the response probability of an exposed user, compared with the counterfactual (un-observable) response-rate of the user if they were not exposed to the ad. In an ideal randomized test, the user is randomly assigned to test or control AFTER the submitted bid is won, and test users are served the ad in the normal way, while control users are not. While this is ideal from a statistical perspective, in practice this approach has the drawback that money spent by advertisers is wasted when a user is assigned to control. At Media Math we have developed a methodology for causal effect measurement where users are assigned to test or control BEFORE bid submission. One challenge here is that not all test-group users are exposed to an ad; only a winning bid results in ad exposure, and the winning population can have a significant bias. This talk will describe our approach to handle this and other challenges to ad impact measurement in this setting, and how we use MCMC Gibbs sampling to arrive at confidence intervals for ad-impact.

Closing the Gap Between Digital Technology and Prevention of Disease Using Data & Analytics

Thursday, February 23, 2017 - 5:00pm to 6:00pm
United States

Columbia Data Science Innovation Seminars

Evan Garmaise, Data Scientist
Junghoon Woo, Data Scientist

In 2016, the Department of Health and Human Services announced the certification of the Diabetes Prevention Program (DPP). The DPP aims to reach 86 million pre-diabetic Medicare participants in the United States through education, training, and lifestyle coaching. According to the physician payment rule recently announced, Medicare will be reimbursing both digital and in-person versions of the DPP; however, it remained unclear how the parameters for the digital version will be set. Due to the relatively short history of digital DPP, little is known regarding the mechanism of weight loss when the services are rendered through a mobile app. This will be critical for CMS to set payment mechanism by outcomes as it announced. To better understand the mechanism of weight loss by digital DPP solutions, and to help CMS make the most informed decision on the payment rule, we have collaborated with one of the few certified digital DPP.

Yes, you really can use this! - Applying Data Science to Real-World Problems

Thursday, March 2, 2017 - 5:00pm to 6:00pm
Schapiro Hall (CEPSR) Davis Auditorium
530 W 120th St.
New York, NY 10027
United States

Columbia Data Science Innovation Seminars

Ben Arancibia, Lead Data Scientist | Booz Allen Hamilton
Dan Liebermann, Lead Associate | Booz Allen Hamilton

Booz Allen Hamilton’s Dan Liebermann and Ben Arancibia will cover what it takes to get data science done in the real world. They will be sharing stories from the trenches – covering experiences and lessons learned from turning data science theory into reality when the problem (and the solution) are far from known. The talk will heavily engage the audience to hear their perspective, and cover the approach Booz Allen took to solve its clients’ problems. The goal is to get the audience thinking about what they would do in these situations and how they would apply their classroom experience.

Understanding What Sticks in the U.S. Presidential Election Race

Tuesday, November 1, 2016 - 1:00pm to 2:00pm
United States

Columbia Data Science Innovation Seminars

The US Presidential election is probably the most significant political event in 2016. The spectacular campaign period has featured many controversial news stories. Many analysts have given their views on how the public image of Clinton and Trump affect the candidates’ success; what kinds of stories stick with a candidate, who earns on a certain story, and what do the candidates’ constantly shifting public images mean for the outcome of the election?

Describing the landscape of political positions and measuring the effects of speeches and events is very difficult to do. This session will feature research from United Minds, a Weber Shandwick company, supported by text analytics technology firm Gavagai.

Estimating Causal Effect of Ads in a Real-Time Bidding Platform

Tuesday, April 26, 2016 - 5:00pm to 6:00pm
Davis Auditorium, 412 CEPSR, Schapiro Center
530 West 120th Street
New York, NY 10027
United States

Columbia Data Science Innovation Seminars

Prasad Chalasani
SVP, Data Science
MediaMath

A real-time bidding platform responds to incoming ad-opportunities ("bid requests") by deciding whether or not to submit a bid and how much to bid. If the submitted bid wins, the user is shown an ad. Advertisers hope that ad-exposure leads to an increased likelihood of a desired action, such as a click or conversion (purchase, etc). So an important quantity that advertisers want to measure is the causal effect of advertising, namely, what is the response probability of an exposed user, compared with the counterfactual (un-observable) response-rate of the user if they were not exposed to the ad. In an ideal randomized test, the user is randomly assigned to test or control AFTER the submitted bid is won, and test users are served the ad in the normal way, while control users are not. While this is ideal from a statistical perspective, in practice this approach has the drawback that money spent by advertisers is wasted when a user is assigned to control. At Media Math we have developed a methodology for causal effect measurement where users are assigned to test or control BEFORE bid submission. One challenge here is that not all test-group users are exposed to an ad; only a winning bid results in ad exposure, and the winning population can have a significant bias. This talk will describe our approach to handle this and other challenges to ad impact measurement in this setting, and how we use MCMC Gibbs sampling to arrive at confidence intervals for ad-impact.

The Cognitive Modeling Paradigm: An Experiment in Casual Inference

Monday, October 26, 2015 - 6:00pm to 7:00pm
Davis Auditorium, Room 412, Shapiro SEPSR
Columbia University
New York, NY 10027
United States

Alex Cosmas
Chief Scientist | Booz Allen Hamilton

The analytics community has invested significant resources in developing effective predictive analytical methods. However, even the most accurate predictive forecasts have limited value unless they can also provide clear action steps to bring about desired results.  In other words, the cause of the data is more important than the data itself.  Alex will introduce causal inference in the context of Bayesian Belief Networks (BBNs).  BBN’s produce accurate predictive forecasts, but with appropriate modeler input are also able to identify causal relationships between variables and pinpoint drivers of desired targets. With causal relationships identified, BBNs may be used in a prescriptive fashion in order to make actionable decisions.  Alex will dive into a case study in the aviation space which identifies causal drivers of daily flight operations on flight delays and allows us to prescribe delay-reduction plans by acting on controllable drivers.

Institute-Industry-Innovation Seminar: Michael Schlee and Mayur Thakur, Goldman Sachs

Thursday, March 26, 2015 - 6:00pm to 7:00pm
Davis Auditorium, Room 412, Shapiro SEPSR
530 West 120th Street
New York, NY 10027
United States

Leveraging Big Data Analytics for Compliance in Financial Institutions

Leveraging Big Data Analytics for Compliance in Financial Institutions with Goldman Sachs

It is critical for a financial institution to comply with government regulations. The cost of non-compliance can result in criminal indictment, multi-billion dollar fines and loss of banking and other licenses. Employees of Compliance departments are responsible for implementation of proper policies, procedures and monitoring to ensure compliance with regulations. This discussion will focus on how Compliance leverages large quantities of data to establish monitoring controls. In particular, we will discuss specific business problems and show how they map into problems in natural language processing, outlier detection, and graph analytics. No prior knowledge of finance will be assumed.

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