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- Faculty Publications (229)
- March 2023
- Technical Note
Companion to: Demand Simulator Spreadsheet Supplement
By: Elie Ofek and Olivier Toubia
Ofek, Elie, and Olivier Toubia. "Companion to: Demand Simulator Spreadsheet Supplement." Harvard Business School Technical Note 523-096, March 2023.
- February 2023
- Teaching Note
SIMmersion: Simulating Crucial Conversations
By: Alison Wood Brooks and Julian Zlatev
Teaching Note for HBS Case No. 923-040. View Details
- February 2023
- Case
SIMmersion: Simulating Crucial Conversations
By: Alison Wood Brooks, Julian Zlatev and F Katelynn Boland
This case introduces readers to SIMmersion, a company founded in 2002 that creates and sells training programs to firms, government agencies, educational institutions, and individuals (B2B and B2C). Their training programs are built around simulations (“sims”) that... View Details
Brooks, Alison Wood, Julian Zlatev, and F Katelynn Boland. "SIMmersion: Simulating Crucial Conversations." Harvard Business School Case 923-040, February 2023.
- February 2023 (Revised March 2024)
- Supplement
Shanty Real Estate: Teaching Note Supplement
By: Michael Luca and Jesse M. Shapiro
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough.... View Details
- 2023
- Working Paper
Distributionally Robust Causal Inference with Observational Data
By: Dimitris Bertsimas, Kosuke Imai and Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is based on distributionally robust optimization and proceeds in two steps. We first... View Details
Bertsimas, Dimitris, Kosuke Imai, and Michael Lingzhi Li. "Distributionally Robust Causal Inference with Observational Data." Working Paper, February 2023.
- 2023
- Working Paper
The Link Between Integrative Bargaining and Leadership Evaluations
By: Julian J. Zlatev and Francis J. Flynn
We draw from implicit leadership theory and the dual concern theory of conflict resolution to posit a link
between negotiation style and leadership evaluations. Specifically, we propose that individuals who are
more skilled at integrative, but not distributive,... View Details
Keywords: Prosocial Behavior; Leadership; Negotiation; Conflict and Resolution; Performance Evaluation
Zlatev, Julian J., and Francis J. Flynn. "The Link Between Integrative Bargaining and Leadership Evaluations." Harvard Business School Working Paper, No. 23-044, January 2023.
- November 2022
- Article
Measuring Inequality beyond the Gini Coefficient May Clarify Conflicting Findings
By: Kristin Blesch, Oliver P. Hauser and Jon M. Jachimowicz
Prior research has found mixed results on how economic inequality is related to various outcomes. These contradicting findings may in part stem from a predominant focus on the Gini coefficient, which only narrowly captures inequality. Here, we conceptualize the... View Details
Keywords: Economic Inequalty; Gini Coefficient; Income Inequality; Equality and Inequality; Social Issues; Health; Status and Position
Blesch, Kristin, Oliver P. Hauser, and Jon M. Jachimowicz. "Measuring Inequality beyond the Gini Coefficient May Clarify Conflicting Findings." Nature Human Behaviour 6, no. 11 (November 2022): 1525–1536.
- October 2022
- Exercise
Shanty Real Estate: Confidential Information for Homebuyer 1
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough.... View Details
Keywords: Data-driven Decision-making; Decisions; Negotiation; Bids and Bidding; Valuation; Consumer Behavior; Real Estate Industry
Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for Homebuyer 1." Harvard Business School Exercise 923-016, October 2022.
- October 2022
- Exercise
Shanty Real Estate: Confidential Information for Homebuyer 2
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough.... View Details
Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for Homebuyer 2." Harvard Business School Exercise 923-017, October 2022.
- October 2022
- Exercise
Shanty Real Estate: Confidential Information for Homebuyer 3
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough.... View Details
Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for Homebuyer 3." Harvard Business School Exercise 923-018, October 2022.
- October 2022
- Exercise
Shanty Real Estate: Confidential Information for iBuyer 1
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough.... View Details
Keywords: Algorithm; Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for iBuyer 1." Harvard Business School Exercise 923-019, October 2022.
- October 2022
- Exercise
Shanty Real Estate: Confidential Information for iBuyer 2
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough.... View Details
Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for iBuyer 2." Harvard Business School Exercise 923-020, October 2022.
- October 2022
- Exercise
Shanty Real Estate: Confidential Information for iBuyer 3
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough.... View Details
Keywords: Algorithm; Decision Choices and Conditions; Decision Making; Measurement and Metrics; Market Timing
Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Confidential Information for iBuyer 3." Harvard Business School Exercise 923-021, October 2022.
- October 2022
- Exercise
Shanty Real Estate: Updated Confidential Information for Homebuyer
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough.... View Details
Keywords: Algorithm; Decision Choices and Conditions; Decision Making; Market Timing; Measurement and Metrics
Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Updated Confidential Information for Homebuyer." Harvard Business School Exercise 923-022, October 2022.
- October 2022
- Exercise
Shanty Real Estate: Updated Confidential Information for iBuyer
By: Michael Luca, Jesse M. Shapiro and Nathan Sun
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough.... View Details
Keywords: Algorithm; Decision Choices and Conditions; Measurement and Metrics; Market Timing; Decision Making
Luca, Michael, Jesse M. Shapiro, and Nathan Sun. "Shanty Real Estate: Updated Confidential Information for iBuyer." Harvard Business School Exercise 923-023, October 2022.
- October–December 2022
- Article
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; AI and Machine Learning; Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
- 2022
- Article
Nonparametric Subset Scanning for Detection of Heteroscedasticity
By: Charles R. Doss and Edward McFowland III
We propose Heteroscedastic Subset Scan (HSS), a novel method for identifying covariates that are responsible for violations of the homoscedasticity assumption in regression settings. Viewing the problem as one of anomalous pattern detection, we use subset scanning... View Details
Doss, Charles R., and Edward McFowland III. "Nonparametric Subset Scanning for Detection of Heteroscedasticity." Journal of Computational and Graphical Statistics 31, no. 3 (2022): 813–823.
- Article
Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details
Keywords: Algorithm Bias; Personalization; Targeting; Generalized Random Forests (GRF); Discrimination; Customization and Personalization; Decision Making; Fairness; Mathematical Methods
Ascarza, Eva, and Ayelet Israeli. "Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)." e2115126119. Proceedings of the National Academy of Sciences 119, no. 11 (March 8, 2022).
- March 2022 (Revised February 2024)
- Case
Applied Intuition: Powering Autonomy
By: Andy Wu, Rocio Wu and Matt Higgins
Applied Intuition, a leader in autonomous vehicle simulation software, has just closed on a $175 million round of Series D financing that values the four-year-old firm at $3.6 billion. With the immediate future secure, CEO Qasar Younis must now chart a strategic course... View Details
Keywords: Autonomous Vehicles; Software; Strategy; Competitive Strategy; Growth and Development Strategy; Valuation; Auto Industry; Technology Industry; California; Detroit
Wu, Andy, Rocio Wu, and Matt Higgins. "Applied Intuition: Powering Autonomy." Harvard Business School Case 722-407, March 2022. (Revised February 2024.)