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- June 2024
- Article
Stereotypes and Belief Updating
By: Katherine B. Coffman, Manuela Collis and Leena Kulkarni
We explore how feedback shapes, and perpetuates, gender gaps in self-assessments. Participants in our experiment take tests of their ability across different domains. We elicit their beliefs of their performance before and after feedback. We find that, even after the... View Details
- April 2024
- Article
Detecting Routines: Applications to Ridesharing CRM
By: Ryan Dew, Eva Ascarza, Oded Netzer and Nachum Sicherman
Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which we define as repeated behaviors with recurring, temporal... View Details
- 2023
- Article
Balancing Risk and Reward: An Automated Phased Release Strategy
By: Yufan Li, Jialiang Mao and Iavor Bojinov
Phased releases are a common strategy in the technology industry for gradually releasing new products or updates through a sequence of A/B tests in which the number of treated units gradually grows until full deployment or deprecation. Performing phased releases in a... View Details
- 2023
- Working Paper
The Customer Journey as a Source of Information
By: Nicolas Padilla, Eva Ascarza and Oded Netzer
In the face of heightened data privacy concerns and diminishing third-party data access,
firms are placing increased emphasis on first-party data (1PD) for marketing decisions.
However, in environments with infrequent purchases, reliance on past purchases 1PD... View Details
- May 2022
- Article
When Harry Fired Sally: The Double Standard in Punishing Misconduct
By: Mark Egan, Gregor Matvos and Amit Seru
We examine gender differences in misconduct punishment in the financial advisory industry. We find evidence of a “gender punishment gap”: following an incident of misconduct, female advisers are 20% more likely to lose their jobs and 30% less likely to find new jobs... View Details
- March 2022
- Article
Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models
By: Fiammetta Menchetti and Iavor Bojinov
Researchers regularly use synthetic control methods for estimating causal effects when a sub-set of units receive a single persistent treatment, and the rest are unaffected by the change. In many applications, however, units not assigned to treatment are nevertheless... View Details
- Article
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
By: Dylan Slack, Sophie Hilgard, Sameer Singh and Himabindu Lakkaraju
As black box explanations are increasingly being employed to establish model credibility in high stakes settings, it is important to ensure that these explanations are accurate and reliable. However, prior work demonstrates that explanations generated by... View Details
- August 2021
- Article
Multiple Imputation Using Gaussian Copulas
By: F.M. Hollenbach, I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward and A. Volfovsky
Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this paper, we present a simple-to-use... View Details
- March 2021
- Article
Bayesian Signatures of Confidence and Central Tendency in Perceptual Judgment
By: Yang Xiang, Thomas Graeber, Benjamin Enke and Samuel Gershman
This paper theoretically and empirically investigates the role of Bayesian noisy cognition in perceptual judgment, focusing on the central tendency effect: the well-known empirical regularity that perceptual judgments are biased towards the center of the... View Details
- Article
Incorporating Interpretable Output Constraints in Bayesian Neural Networks
By: Wanqian Yang, Lars Lorch, Moritz Graule, Himabindu Lakkaraju and Finale Doshi-Velez
Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and fairness. We introduce a novel probabilistic framework for reasoning with such constraints... View Details
- March 2020
- Article
Diagnosing Missing Always at Random in Multivariate Data
By: Iavor I. Bojinov, Natesh S. Pillai and Donald B. Rubin
Models for analyzing multivariate data sets with missing values require strong, often assessable, assumptions. The most common of these is that the mechanism that created the missing data is ignorable—a twofold assumption dependent on the mode of inference. The first... View Details
- March 2019
- Article
A Structural Analysis of the Role of Superstars in Crowdsourcing Contests
By: Shunyuan Zhang, Param Singh and Anindya Ghose
We investigate the long-term impact of competing against superstars in crowdsourcing contests. Using a unique 50-month longitudinal panel data set on 1677 software design crowdsourcing contests, we illustrate a learning effect where participants are able to improve... View Details
- June 2018
- Article
Personal and Social Usage: The Origins of Active Customers and Ways to Keep Them Engaged
By: Clarence Lee, Elie Ofek and Thomas Steenburgh
We study how digital service firms can develop an active customer base, focusing on two questions. First, how does the way that customers use the service postadoption to meet their own needs (personal usage) and to interact with one another (social usage) vary across... View Details
- August 2017
- Article
Incentives versus Reciprocity: Insights from a Field Experiment
By: Doug J. Chung and Das Narayandas
We conduct a field experiment in which we vary the sales force compensation scheme at an Asian enterprise that sells consumer durable goods. With variation generated by the experimental treatments, we model sales force performance to identify the effectiveness of... View Details
- 2016
- Working Paper
Paying (for) Attention: The Impact of Information Processing Costs on Bayesian Inference
By: Scott Duke Kominers, Xiaosheng Mu and Alexander Peysakhovich
Human information processing is often modeled as costless Bayesian inference.
However, research in psychology shows that attention is a computationally costly and potentially limited resource. We study a Bayesian individual for whom computing posterior beliefs is... View Details
- 2016
- Article
Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making
By: Himabindu Lakkaraju and Jure Leskovec
- 2016
- Article
Does volunteering improve well-being?
By: A.V. Whillans, Scott C. Seider, Lihan Chen, Ryan J. Dwyer, Sarah Novick, Kathryn J. Gramigna, Brittany A. Mitchell, Victoria Savalei, Sally S. Dickerson and Elizabeth W. Dunn
Does volunteering causally improve well-being? To empirically test this question, we examined one instantiation of volunteering that is common at post-secondary institutions across North America: community service learning (CSL). CSL is a form of experiential learning... View Details
- 2015
- Working Paper
Incentives versus Reciprocity: Insights from a Field Experiment
By: Doug J. Chung and Das Narayandas
We conduct a field experiment in which we vary the sales force compensation scheme at an Asian enterprise that sells consumer durable goods. With variation generated by the experimental treatments, we model sales force performance to identify the effectiveness of... View Details
- March 2015
- Article
Signaling to Partially Informed Investors in the Newsvendor Model
By: William Schmidt, Vishal Gaur, Richard Lai and Ananth Raman
We investigate a puzzling phenomenon in which firms make investment decisions that purposefully do not maximize expected profits. Using an extension to the newsvendor model, we focus on a relatively common scenario in which the firm's investor has imperfect information... View Details
- 2015
- Article
A Bayesian Framework for Modeling Human Evaluations
By: Himabindu Lakkaraju, Jure Leskovec, Jon Kleinberg and Sendhil Mullainathan