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  • June 2022
  • Article

The Use and Misuse of Patent Data: Issues for Finance and Beyond
By: Josh Lerner and Amit Seru
Patents and citations are powerful tools for understanding innovation increasingly used in financial economics (and management research more broadly). Biases may result, however, from the interactions between the truncation of patents and citations and the changing...  View Details
Keywords: Patents; Analytics and Data Science; Corporate Finance; Research
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  • 2022
  • Working Paper

Causal Inference During A Pandemic: Evidence on the Effectiveness of Nebulized Ibuprofen as an Unproven Treatment for COVID-19 in Argentina
By: Sebastian Calonico, Rafael Di Tella and Juan Cruz Lopez Del Valle
Many medical decisions during the pandemic were made without the support of causal evidence obtained in clinical trials. We study the case of nebulized ibuprofen (NaIHS), a drug that was extensively used on COVID-19 patients in Argentina amidst wild claims about its...  View Details
Keywords: COVID-19; Drug Treatment; Health Pandemics; Health Care and Treatment; Decision Making; Outcome or Result; Argentina
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  • March 2022 (Revised March 2022)
  • Module Note

Statistical Inference
By: Iavor I. Bojinov, Michael Parzen and Paul J. Hamilton
This note provides an overview of statistical inference for an introductory data science course. First, the note discusses samples and populations. Next the note describes how to calculate confidence intervals for means and proportions. Then it walks through the logic...  View Details
Keywords: Data Science; Statistics; Mathematical Methods; Analytics and Data Science
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  • 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
Keywords: Causal Inference; Partial Interference; Synthetic Controls; Bayesian Structural Time Series; Mathematical Methods
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  • 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
Keywords: Black Box Explanations; Bayesian Modeling; Decision Making; Risk and Uncertainty; Information Technology
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  • Article

Behavioral and Neural Representations en route to Intuitive Action Understanding
By: Leyla Tarhan, Julian De Freitas and Talia Konkle
When we observe another person’s actions, we process many kinds of information—from how their body moves to the intention behind their movements. What kinds of information underlie our intuitive understanding about how similar actions are to each other? To address this...  View Details
Keywords: Action Perception; Intuitive Similarity; Multi-arrangement; fMRI; Representational Similarity Analysis; Behavior; Perception
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  • October 2021
  • Article

Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Nicolas Padilla and Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can...  View Details
Keywords: Customer Management; Targeting; Deep Exponential Families; Probabilistic Machine Learning; Cold Start Problem; Customer Relationship Management; Programs; Consumer Behavior; Analysis
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  • September 2021
  • Article

Learning from Deregulation: The Asymmetric Impact of Lockdown and Reopening on Risky Behavior During COVID-19
By: Edward L. Glaeser, Ginger Zhe Jin, Michael Luca and Benjamin T. Leyden
During the COVID-19 pandemic, states issued and then rescinded stay-at-home orders that restricted mobility. We develop a model of learning by deregulation, which predicts that lifting stay-at-home orders can signal that going out has become safer. Using restaurant...  View Details
Keywords: COVID-19; Lockdown; Reopening; Impact; Coronavirus; Public Health Measures; Mobility; Health Pandemics; Governing Rules, Regulations, and Reforms; Consumer Behavior
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  • May 2021
  • Article

Ideology and Composition Among an Online Crowd: Evidence From Wikipedians
By: Shane Greenstein, Grace Gu and Feng Zhu
Online communities bring together participants from diverse backgrounds and often face challenges in aggregating their opinions. We infer lessons from the experience of individual contributors to Wikipedia articles about U.S. politics. We identify two factors that...  View Details
Keywords: User Segregation; Online Community; Contested Knowledge; Collective Intelligence; Ideology; Bias; Wikipedia; Knowledge Sharing; Perspective; Government and Politics
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  • May 19, 2021
  • Article

Measuring the Impact of #MeToo on Gender Equity in Hollywood
By: Hong Luo and Laurina Zhang
The #MeToo movement has brought issues of sexual harassment and gender inequities to the forefront around the world. But how much of a tangible impact has it had on the experiences of women in the workplace? In this piece, the authors discuss their research that...  View Details
Keywords: #MeToo Movement; Gender Equity; Creative Industries; Impact; Gender; Equality and Inequality; Film Entertainment; Social Issues
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  • 2021
  • Working Paper

Population Interference in Panel Experiments
By: Iavor I Bojinov, Kevin Wu Han and Guillaume Basse
The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit's outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in...  View Details
Keywords: Finite Population; Potential Outcomes; Dynamic Causal Effects; Mathematical Methods
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  • March 2021
  • Article

The Impact of the General Data Protection Regulation on Internet Interconnection
By: Ran Zhuo, Bradley Huffaker, KC Claffy and Shane Greenstein
The Internet comprises thousands of independently operated networks, where bilaterally negotiated interconnection agreements determine the flow of data between networks. The European Union’s General Data Protection Regulation (GDPR) imposes strict restrictions on...  View Details
Keywords: Personal Data; Privacy Regulation; GDPR; Interconnection Agreements; Internet and the Web; Governing Rules, Regulations, and Reforms
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  • 2021
  • Working Paper

Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed...  View Details
Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; Analysis; Theory; Measurement and Metrics; Performance Consistency
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  • 2021
  • Article

Consumer Disclosure
By: Tami Kim, Kate Barasz and Leslie John
As technological advances enable consumers to share more information in unprecedented ways, today’s disclosure takes on a variety of new forms, triggering a paradigm shift in what “disclosure” entails. This review introduces two factors to conceptualize consumer...  View Details
Keywords: Disclosure; Passive Disclosure; Information; Internet and the Web; Consumer Behavior; Situation or Environment
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  • 2022
  • Working Paper

Inattentive Inference
By: Thomas Graeber
This paper studies how people infer a state of the world from information structures that include additional, payoff-irrelevant states. For example, learning someone’s effort from their observable performance may require accounting for the otherwise irrelevant role of...  View Details
Keywords: Belief Formation; Attention; Bounded Rationality; Values and Beliefs; Information; Mathematical Methods
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  • 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
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  • October 2020
  • Article

Overcoming Resource Scarcity: Consumers' Response to Gifts Intending to Save Time and Money
By: Alice Lee-Yoon, Grant Donnelly and A.V. Whillans
Consumers feel increasingly pressed for time and money. Gifts have the potential to reduce scarcity in recipients’ lives, yet little is known about how recipients perceive gifts given with the intention of saving them time or money. Across four studies (N=1,403), we...  View Details
Keywords: Scarcity; Status; Time; Gift Giving; Status and Position; Money; Attitudes; Emotions
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  • 2020
  • Working Paper

Design and Analysis of Switchback Experiments
By: Iavor I Bojinov, David Simchi-Levi and Jinglong Zhao
In switchback experiments, a firm sequentially exposes an experimental unit to a random treatment, measures its response, and repeats the procedure for several periods to determine which treatment leads to the best outcome. Although practitioners have widely adopted...  View Details
Keywords: Switchback Experiments; Design; Analysis; Mathematical Methods
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  • Article

The Importance of Being Causal
By: Iavor I Bojinov, Albert Chen and Min Liu
Causal inference is the study of how actions, interventions, or treatments affect outcomes of interest. The methods that have received the lion’s share of attention in the data science literature for establishing causation are variations of randomized experiments....  View Details
Keywords: Causal Inference; Observational Studies; Cross-sectional Studies; Panel Studies; Interrupted Time-series; Instrumental Variables
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  • June 2020
  • Article

In Generous Offers I Trust: The Effect of First-offer Value on Economically Vulnerable Behaviors
By: M. Jeong, J. Minson and F. Gino
Negotiation scholarship espouses the importance of opening a bargaining situation with an aggressive offer, given the power of first offers to shape concessionary behavior and outcomes. In our research, we identify a surprising consequence to this common prescription....  View Details
Keywords: Attribution; Interpersonal Interaction; Judgment; Social Interaction; Inference; Open Data; Open Materials; Preregistered; Negotiation Offer; Strategy; Behavior; Interpersonal Communication; Trust; Outcome or Result
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