Publications

November 2019
Article

How Do Sales Efforts Pay Off? Dynamic Panel Data Analysis in the Nerlove-Arrow Framework

By: Doug J. Chung, Byungyeon Kim and Byoung G. Park

This paper evaluates the short- and long-term value of sales representatives’ detailing visits to different types of physicians. By understanding the dynamic effect of sales calls across heterogeneous physicians, we provide guidance on the design of optimal call... View Details

Keywords: Nerlove-Arrow Framework; Stock-of-goodwill; Dynamic Panel Data; Serial Correlation; Instrumental Variables; Sales Effectiveness; Detailing; Analytics and Data Science; Sales; Analysis; Performance Effectiveness; Pharmaceutical Industry

February 2011
Supplement

Dataset for "Slots, Tables, and All That Jazz: Managing Customer Profitability at the MGM Grand Hotel" (CW)

By: Dennis Campbell and Francisco de Asis Martinez-Jerez

Datasets of gaming and hotel customers to perform analysis for the case. View Details

Keywords: Analytics and Data Science; Games, Gaming, and Gambling; Las Vegas

May–June 2025
Article

Slowly Varying Regression Under Sparsity

By: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Lingzhi Li and Omar Skali Lami

We consider the problem of parameter estimation in slowly varying regression models with sparsity constraints. We formulate the problem as a mixed integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem... View Details

Keywords: Mathematical Methods; Analytics and Data Science

2024
Working Paper

Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python

By: Melissa Ouellet and Michael W. Toffel

This paper describes a range of best practices to compile and analyze datasets, and includes some examples in Stata, R, and Python. It is meant to serve as a reference for those getting started in econometrics, and especially those seeking to conduct data analyses in... View Details

Keywords: Empirical Methods; Empirical Operations; Statistical Methods And Machine Learning; Statistical Interferences; Research Analysts; Analytics and Data Science; Mathematical Methods

SSRN

May 8, 2020
Article

Which Covid-19 Data Can You Trust?

By: Satchit Balsari, Caroline Buckee and Tarun Khanna

The COVID-19 pandemic has produced a tidal wave of data, but how much of it is any good? And as a layperson, how can you sort the good from the bad? The authors suggest a few strategies for dividing the useful data from the misleading: Beware of data that’s too broad... View Details

Keywords: COVID-19 Pandemic; Health Pandemics; Analytics and Data Science

Register to Read

February 2021
Technical Note

Probability Distributions

By: Michael Parzen and Paul Hamilton

This technical note introduces students to the concept of random variables, and from there the normal and binomial distributions. After a brief introduction to random variables, the note describes the standard properties of the normal distribution: a single peak, and a... View Details

Keywords: Analysis; Risk and Uncertainty; Theory; Analytics and Data Science

December 2011
Article

Data Impediments to Empirical Work on Health Insurance Markets

By: Leemore S. Dafny, David Dranove, Frank Limbrock and Fiona Scott Morton

We compare four datasets that researchers might use to study competition in the health insurance industry. We show that the two datasets most commonly used to estimate market concentration differ considerably from each other (both in levels and in changes over time),... View Details

Keywords: Competition; Analytics and Data Science; Market Participation; Insurance Industry

2017
Chapter

Venture Capital Data: Opportunities and Challenges

By: Steven N. Kaplan and Josh Lerner

This paper describes the available data and research on venture capital investments and performance. We comment on the challenges inherent in those data and research as well as possible opportunities to do better. View Details

Keywords: Venture Capital; Analytics and Data Science; Research

February 1994
Background Note

Causal Inference

By: Arthur Schleifer Jr.

Discusses what causation is and what one can (and cannot) learn about causation from observational (nonexperimental) data. View Details

Keywords: Decision Making; Analytics and Data Science; Interests

2023
Working Paper

PRIMO: Private Regression in Multiple Outcomes

By: Seth Neel

We introduce a new differentially private regression setting we call Private Regression in Multiple Outcomes (PRIMO), inspired the common situation where a data analyst wants to perform a set of l regressions while preserving privacy, where the covariates... View Details

Keywords: Analytics and Data Science; Mathematical Methods

April 2023
Technical Note

An Art & A Science: How to Apply Design Thinking to Data Science Challenges

By: Michael Parzen, Eddie Lin, Douglas Ng and Jessie Li

We hear it all the time as managers: “what is the data that backs up your decisions?” Even local mom-and-pop shops now have access to complex point-of-sale systems that can closely track sales and customer data. Social media influencers have turned into seven-figure... View Details

Keywords: Decision Making; Framework; Analytics and Data Science

March 2022 (Revised January 2025)
Technical 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

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

Keywords: Missing Data; Diagnostic Tools; Sensitivity Analysis; Hypothesis Testing; Missing At Random; Row Exchangeability; Analytics and Data Science; Mathematical Methods

2023
Article

Experimental Evaluation of Individualized Treatment Rules

By: Kosuke Imai and Michael Lingzhi Li

The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a... View Details

Keywords: Causal Inference; Heterogeneous Treatment Effects; Precision Medicine; Uplift Modeling; Analytics and Data Science; AI and Machine Learning

September 2016 (Revised March 2017)
Module Note

Strategy Execution Module 3: Using Information for Performance Measurement and Control

By: Robert Simons

This module reading explains how managers use information to control critical business processes and outcomes. The analysis begins by illustrating how managers use information to communicate goals and track performance. Then the focus turns to the choices that managers... View Details

Keywords: Management Control Systems; Implementing Strategy; Strategy Execution; Organization Process; Feedback Model; Innovation; Uses Of Information; Big Data; Benchmarking; Decision Making; Information; Performance Evaluation; Analytics and Data Science

October 2000 (Revised April 2003)
Background Note

Project Finance Research, Data, and Information Sources

By: Benjamin C. Esty and Fuaad Qureshi

Documents the major sources of project finance research and data. It is to be a reference guide for MBA students writing for the elective curriculum course, Large-scale Investment, and for others interested in the field of project finance. View Details

Keywords: Analytics and Data Science; Project Finance; Research; Investment

April 2001
Article

Academic-Practitioner Collaboration in Management Research: A Case of Cross-Profession Collaboration

By: T. M. Amabile, C. Patterson, Jennifer Mueller, T. Wojcik, P. Odomirok, M. Marsh and S. Kramer

We present a case of academic-practitioner research collaboration to illuminate three potential determinants of the success of such cross-profession collaborations: collaborative team characteristics, collaboration environment characteristics, and collaboration... View Details

Keywords: Research; Cases; Analytics and Data Science; Theory

2022
Article

Data Poisoning Attacks on Off-Policy Evaluation Methods

By: Elita Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin and Himabindu Lakkaraju

Off-policy Evaluation (OPE) methods are a crucial tool for evaluating policies in high-stakes domains such as healthcare, where exploration is often infeasible, unethical, or expensive. However, the extent to which such methods can be trusted under adversarial threats... View Details

Keywords: Analytics and Data Science; Cybersecurity; Mathematical Methods

2019
Article

Can Big Data Improve Firm Decision Quality? The Role of Data Quality and Data Diagnosticity

By: Maryam Ghasemaghaei and Goran Calic

Anecdotal evidence suggests that, despite the large variety of data, the huge volume of generated data, and the fast velocity of obtaining data (i.e., big data), quality of big data is far from perfect. Therefore, many firms defer collecting and integrating big data as... View Details

Keywords: Big Data; Analytics and Data Science; Decisions; Quality