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  • 2022
  • Working Paper

What Would It Mean for a Machine to Have a Self?
By: Julian De Freitas, Ahmet Kaan Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum and Tomer Ullman
What would it mean for autonomous AI agents to have a ‘self’? One proposal for a minimal notion of self is a representation of one’s body spatio-temporally located in the world, with a tag of that representation as the agent taking actions in the world. This turns...  View Details
Keywords: Self; AI; Games; Reinforcement Learning; Avatar; AI and Machine Learning
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  • 2022
  • Working Paper

Buy Now, Pay Later Credit: User Characteristics and Effects on Spending Patterns
By: Marco Di Maggio, Justin Katz and Emily Williams
Firms offering "buy now, pay later" (BNPL) point-of-sale installment loans with minimal underwriting and low interest have captured a growing fraction of the market for short-term unsecured consumer credit. We provide a detailed look into the US BNPL market by...  View Details
Keywords: Household Finance; Fintech; Consumer Credit; Credit; Consumer Behavior
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  • 2022
  • Working Paper

Failing Just Fine: Assessing Careers of Venture Capital-backed Entrepreneurs via a Non-wage Measure
By: Natee Amornsiripanitch, Paul Gompers, George Hu, Will Levinson and Vladimir Mukharlyamov
This paper proposes a non-pecuniary measure of career achievement, Seniority. Based on a database of over 5 million resumes, this metric exploits the variation in job titles and how long they take to attain. When non-monetary factors influence career choice, inference...  View Details
Keywords: Career Outcomes; Founders; Personal Development and Career; Venture Capital; Entrepreneurship
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  • June 2022 (Revised July 2022)
  • Module Note

Causal Inference
By: Iavor Bojinov, Michael Parzen and Paul Hamilton
This note provides an overview of causal inference for an introductory data science course. First, the note discusses observational studies and confounding variables. Next the note describes how randomized experiments can be used to account for the effect of...  View Details
Keywords: Causal Inference; Causality; Experiment; Experimental Design; Data Science; Analytics and Data Science
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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 July 2022)
  • Module Note

Statistical Inference
By: Iavor I. Bojinov, Michael Parzen and Paul 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 Modeling; 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
  • 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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  • 2020
  • Working Paper

Look the Part? The Role of Profile Pictures in Online Labor Markets
By: Isamar Troncoso and Lan Luo
Profile pictures are a key component of many freelancing platforms, a design choice that can impact hiring and matching outcomes. In this paper, we examine how appearance-based perceptions of a freelancer's fit for the job (i.e., whether a freelancer "looks the part"...  View Details
Keywords: Freelancers; Gig Workers; Recruitment; Perception; Performance; Prejudice and Bias; Decision Choices and Conditions
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