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- September 16, 2022
- Article
A Causal Test of the Strength of Weak Ties
By: Karthik Rajkumar, Guillaume Saint-Jacques, Iavor I. Bojinov, Erik Brynjolfsson and Sinan Aral
The authors analyzed data from multiple large-scale randomized experiments on LinkedIn’s People You May Know algorithm, which recommends new connections to LinkedIn members, to test the extent to which weak ties increased job mobility in the world’s largest...
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- August 29, 2022
- Other Article
Income Inequality Is Rising. Are We Even Measuring It Correctly?
By: Jon M. Jachimowicz, K. Blesch and Oliver P. Hauser
Income inequality is on the rise in many countries around the world, according to the United Nations. What’s more, disparities in global income were exacerbated by the COVID-19 pandemic, with some countries facing greater economic losses than others.
Policymakers...
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- August 2022
- Supplement
Zalora: Data-Driven Pricing Recommendations
By: Ayelet Israeli
This exercise can be used in conjunction with the main case "Zalora: Data-Driven Pricing" to facilitate class discussion without requiring data analysis from the students. Instead, the exercise presents reports that were created by the data science team to answer the...
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Keywords:
Pricing;
Pricing Algorithms;
Dynamic Pricing;
Ecommerce;
Pricing Strategy;
Pricing And Revenue Management;
Apparel;
Singapore;
Startup;
Demand Estimation;
Data Analysis;
Data Analytics;
Exercise;
Price;
Internet and the Web;
Apparel and Accessories Industry;
Retail Industry;
Fashion Industry;
Singapore
- 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...
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- Article
Do the Right Firms Survive Bankruptcy?
By: Samuel Antill
In U.S. Chapter 11 bankruptcy cases, firms are either reorganized, acquired, or liquidated. I show that decisions to liquidate often reduce creditor recovery, costing creditors billions of dollars every year. I exploit the within-district random assignment of...
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- March 2022 (Revised July 2022)
- Module Note
Exploratory Data Analysis
This module note provides an overview of exploratory data analysis for an introduction to data science course. It begins by defining the term "data", and then describes the different types of data that companies work with (structured v. unstructured, categorical v....
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- March 2022 (Revised July 2022)
- Module Note
Prediction & Machine Learning
This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional...
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- March 2022 (Revised July 2022)
- Module Note
Statistical Inference
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...
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- March 2022
- Article
Assessing the Impact of the COVID-19 Pandemic on Clinician Ambulatory Electronic Health Record Use
By: A Jay Holmgren, Lance Downing, Mitchell Tang, Christopher Sharp, Christopher Longhurst and Robert S. Huckman
Objective: The COVID-19 pandemic changed clinician electronic health record (EHR) work in a multitude of ways. To evaluate how, we measure ambulatory clinician EHR use in the United States throughout the COVID-19 pandemic.
Materials and Methods: We use EHR... View Details
Materials and Methods: We use EHR... View Details
- 2022
- Chapter
State-Formation, Statist Islam, and Regime Instability: Evidence from Turkey
By: Kristin Fabbe
Religion, and particularly the forces of political Islam and state secularism, have been central to discussions of regime stability in the Turkish case. Intense polarization, political instability, and military interventions have propelled Turkey into crisis about once...
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- 2021
- Working Paper
The Luck of the Draw: The Causal Effect of Physicians on Birth Outcomes
By: Arlen Guarin, Christian Posso, Estefania Saravia and Jorge Tamayo
Identifying the effect of physicians’ skills on health outcomes is a challenging task due to the nonrandom sorting between physicians and hospitals. We overcome this challenge by exploiting a Colombian government program that randomly assigned 2,126 physicians to 618...
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- 2021
- Working Paper
How Important is Editorial Gatekeeping? Evidence from Top Biomedical Journals
We examine editors' influence on the scientific content of academic journals by unpacking the role of three major forces: journals' missions, aggregate supply of and demand for specific topics, and scientific homophily via editorial gatekeeping. In a sample of top...
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- August 2021
- Article
Crowdsourcing Memories: Mixed Methods Research by Cultural Insiders-Epistemological Outsiders
By: Tarun Khanna, Karim R. Lakhani, Shubhangi Bhadada, Nabil Khan, Saba Kohli Davé, Rasim Alam and Meena Hewett
This paper examines the role that the two lead authors’ personal connections played in the research methodology and data collection for the Partition Stories Project—a mixed-methods approach to revisiting the much-studied historical trauma of the Partition of British...
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- 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...
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- June 2021
- Technical Note
Introduction to Linear Regression
By: Michael Parzen and Paul Hamilton
This technical note introduces (from an applied point of view) the theory and application of simple and multiple linear regression. The motivation for the model is introduced, as well as how to interpret the summary output with regard to prediction and statistical...
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- May 2021
- Article
Fifty Shades of QE: Comparing Findings of Central Bankers and Academics
By: Brian Fabo, Marina Jančoková, Elisabeth Kempf and Ľuboš Pástor
We compare the findings of central bank researchers and academic economists regarding the macroeconomic effects of quantitative easing (QE). We find that central bank papers find QE to be more effective than academic papers do. Central bank papers report larger effects...
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- 2020
- Working Paper
Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective
We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables—outnumbering the...
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- Mar 2021
- Conference Presentation
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
By: Seth Neel, Aaron Leon Roth and Saeed Sharifi-Malvajerdi
We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are able to handle an arbitrarily long sequence of adversarial updates while promising both...
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- 2021
- Working Paper
First Law of Motion: Influencer Video Advertising on TikTok
By: Jeremy Yang, Juanjuan Zhang and Yuhan Zhang
This paper engineers an intuitive feature that is predictive of the causal effect of influencer video advertising on product sales. We propose the concept of m-score, a summary statistic that captures the extent to which a product is advertised in the most engaging...
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- February 2021
- Tutorial
Assessing Prediction Accuracy of Machine Learning Models
By: Michael Toffel and Natalie Epstein
This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and...
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