E. Shin Oblander
About Me:
I am a doctoral candidate in the Marketing division of Columbia Business School. I am on the academic job market in 2023.
I am primarily a methodologist, working on both statistical/econometric methods and probabilistic machine learning. In past work, I have developed methods for selection correction and causal inference in modeling customer purchase decisions. Currently, I am working on projects based around representation learning methods for unstructured data and empirical modeling of boundedly rational behavior.
My research has been published in Marketing Science and I am in the top 1.1% of authors on SSRN by downloads. My work has been featured in news outlets such as the Wall Street Journal and New York Magazine.
My name, Shin, is pronounced like "sheen" but with the "ee" shortened. My pronouns are they/them.
Curriculum Vitae:
See my most current CV here.
Contact Info:
Email: eoblander23@gsb.columbia.edu
Research
I come from a customer relationship management (CRM) background, and much of my early work has been inspired by this perspective, developing methodologies for data fusion, selection correction, and causal inference in modeling customer purchase decisions. More generally, I am interested in developing methodologies that allow for the analysis of complex and/or non-standard data structures, such as representation learning methods for unstructured data.
Recently, I am also interested in competitive video games (e-sports) as an empirical context to study strategic human behavior. Currently, I am studying competitive decisions in an e-sports context, developing representation learning tools to enable behavioral analysis of how agents learn and make strategic decisions based on past experience in games with large/complex action spaces.
Working Papers
Oblander, Shin (2023). "Representation Learning for Behavioral Analysis of Complex Games." Working paper, draft available upon request.
Abstract: Many phenomena studied in marketing and economics are analyzed through the lens of non-cooperative games. Researchers often study empirical behavior of agents in simple games conducted in the lab, but research on complex real-world competitive settings with extensive action spaces and intricate payoff structures is rare due to methodological challenges and data limitations. To bridge this gap, I develop a novel neural network architecture that enables behavioral analysis of complex games by estimating a game's payoff structure (e.g., win probabilities between pairs of actions) while simultaneously mapping agent actions to a lower-dimensional latent space. I structure the neural network to enforce that the latent space encodes strategic similarities between actions in a smooth, linear manner. I apply my method to analyze a unique dataset of over 11 million matches played in a competitive video game with a large array of actions and complex strategic interactions. I find that players select actions that counterfactually would have performed better against recent opponents, demonstrating model-based reasoning. Still, players overrely on simple heuristics relative to model-based reasoning to an extent that is similar to findings reported in lab settings. I find that noisy and biased decision-making leads to frequent selection of suboptimal actions, which corresponds to lower player engagement. This demonstrates the limits of player sophistication when making complex competitive decisions and suggests that platforms hosting competitions may benefit from interventions that enable players to improve their decision-making.
Published and Forthcoming Papers
Oblander, Shin and Daniel Minh McCarthy (2023). "Frontiers: Estimating the Long-Term Impact of Major Events on Consumption Patterns: Evidence from COVID-19." Marketing Science, Articles in Advance.
McCarthy, Daniel Minh and Elliot Shin Oblander (2021). "Scalable Data Fusion with Selection Correction: An Application to Customer Base Analysis." Marketing Science, 40(3), 459-480.
Winner of 2019 Marketing Strategy Meets Wall Street Conference's Best Paper Award
Oblander, Elliot Shin, Sunil Gupta, Carl F. Mela, Russell S. Winer, and Donald R. Lehmann (2020). "The Past, Present, and Future of Customer Management." Marketing Letters, 31(2), 125-136.
Oblander, Elliot, Sojung Carol Park, and Jean Lemaire (2016). "The Cost of High Suicide Rates in Japan and the Republic of Korea: Reduced Life Expectancies." Asia-Pacific Population Journal, 31(2), 21-44.
Education (click for more details)
I am currently a doctoral candidate in quantitative marketing at Columbia Business School. Prior to graduate school, I completed my undergraduate degree at the Wharton School of the University of Pennsylvania, where I studied statistics and actuarial science.
PhD Marketing (Quantitative track), Graduate School of Business, Columbia University (August 2018 - Present)
MPhil conferred February 2021
Advisors: Asim Ansari and Oded Netzer
Dissertation Committee: Olivier Toubia, David Blei, and Michael Woodford
Manager of CBS Quantitative Marketing Lab, member of Columbia Economics Cognition and Decision Lab
GPA 10.62/11.00
BS Economics, The Wharton School of the University of Pennsylvania (August 2013 – May 2018)
Honors: Summa Cum Laude, Dean's Award for Excellence, Dean's List 2013-2018
Concentrations: Statistics and Actuarial Science
Minors: Mathematics and Japanese Studies
GPA 3.95/4.00
Exchange Student, Faculty of Economics at Hitotsubashi University (March 2017 – August 2017)
GPA 4.30/4.30
Teaching (click for more details)
Beyond research, I am passionate about teaching and pedagogy, striving to make technical quantitative content accessible to students. To my knowledge, I am the first PhD student at Columbia Business School to develop and teach their own credit-bearing course.
Pedagogical Training
I have taken extensive training on evidence-based pedagogy and inclusive teaching practices through Columbia's Center for Teaching and Learning. I was the first Lead Teaching Fellow from the business school in 2020-2021 and became the first business school student to complete the Advanced track of the Teaching Development Program certification in 2023.
Instruction (Columbia Business School)
In 2020, I developed and taught a new course on probability theory and statistical estimation, called Statistical Modeling and Decision Making, as part of the core curriculum for the MS in Marketing Science program; I continued to teach this course in 2021 and 2022. In 2023, my course was recommended for approval by the CBS Curriculum and Instruction Committee to become a regularly offered course.
Fall 2020, Fall 2021, Fall 2022: Statistical Modeling and Decision Making
Columbia Business School MS in Marketing Science course
Instructor rating: 4.7/5 (2020), 4.6/5 (2021), 4.4/5 (2022)
Some cherry-picked comments from past students:
"The passion and love you put into teaching and research are just inspirational, and you always find a way to make complex concepts accessible."
"[Shin] was helpful and always knew the answer to literally every question someone asked."
"Hands down one of the best stats teachers."
Teaching Assistantships (Columbia Business School)
Fall 2021, Fall 2022: Analytics in Action, Professors Daniel Guetta and Brett Martin
MBA course
Spring 2022: Customer Management, Professor Kinshuk Jerath
MS course
Fall 2021: Applied Multivariate Statistics, Professor Kamel Jedidi
MS/PhD course
Spring 2021: Pricing Strategies, Professor Asim Ansari
MS course
Teaching Assistantships (The Wharton School)
Spring 2016, Summer 2016, Spring 2017, Summer 2017, Fall 2017, and Spring 2018: Applied Probability Models in Marketing, Professor Peter Fader
Spring 2016, Spring 2017, and Spring 2018: Undergraduate/MBA cross-listed course
Summer 2016, Summer 2017, and Fall 2017: Executive MBA course
Fall 2015: Risk Management, Professor Gregory Nini
Undergraduate/MBA cross-listed course
Fellowships, Scholarships, and Awards (click for more details)
AMA-Sheth Foundation Doctoral Consortium Fellow, 2022.
MSI Research Grant (for Estimating the Long-Term Impact of Major Events on Consumption Patterns: Evidence from COVID-19), 2021.
Deming Center Doctoral Fellow, 2020-2021.
Columbia Business School Amanda and Harold J Rudolph Fellow, 2020-2021.
Columbia University Center for Teaching and Learning (CTL) Lead Teaching Fellow (LTF), 2020-2021.
ISMS Doctoral Consortium Fellow, 2019.
Marketing Strategy Meets Wall Street Conference's Best Paper Award (for Scalable Data Fusion with Selection Correction: An Application to Customer Base Analysis), 2019.
Columbia University Provost Diversity Fellow, 2018.
Other fun facts (click for more details)
The header picture has nothing to do with my research; I just think trains look cool. I did, however, take the photo at 125th Street Station, right by the CBS campus.
I am an avid transit and city planning enthusiast, and I like trainspotting in my free time. This video explaining the Tokyo area rail network heavily features footage shot by me!
I am a fan of Harajuku street fashion and try to incorporate elements of it into my own style. I sporadically post my outfits on my Instagram.
My name is written as 森 in Japanese, meaning "forest."
Sometimes I like to make satirical redesigns of school logos, such as my proposed rebranding for CBS below (somehow the school hasn't decided to adopt it yet).
