Technology & Operations Management

- 2024
- Working Paper
Heterogeneous Treatment Effects in Panel Data

By: Retsef Levi, Elisabeth Paulson, Georgia Perakis and Emily Zhang

We address a core problem in causal inference: estimating heterogeneous treatment effects using panel data with general treatment patterns. Many existing methods either do not utilize the potential underlying structure in panel data or have limitations in the allowable treatment patterns. In this work, we propose and evaluate a new method that first partitions observations into disjoint clusters with similar treatment effects using a regression tree, and then leverages the (assumed) low-rank structure of the panel data to estimate the average treatment effect for each cluster. Our theoretical results establish the convergence of the resulting estimates to the true treatment effects. Computation experiments with semi-synthetic data show that our method achieves superior accuracy compared to alternative approaches, using a regression tree with no more than 40 leaves. Hence, our method provides more accurate and interpretable estimates than alternative methods.
- 2024
- Working Paper
Heterogeneous Treatment Effects in Panel Data

By: Retsef Levi, Elisabeth Paulson, Georgia Perakis and Emily Zhang

We address a core problem in causal inference: estimating heterogeneous treatment effects using panel data with general treatment patterns. Many existing methods either do not utilize the potential underlying structure in panel data or have limitations in the allowable treatment patterns. In this work, we propose and evaluate a new method that...
- January 2026
- Case
Despegar: Will AI Power the Next Takeoff?

By: Antonio Moreno and Karina Souza

In August 2025, Despegar, Latin America’s leading online travel agency, was closely monitoring AI’s development and its impact. As agentic AI threatened to reshape travel distribution by potentially bypassing intermediaries, Despegar’s CEO, Damián Scokin, faced a strategic tension: whether to accelerate AI adoption to remain competitive while risking commoditization—amid the company’s recent integration into Prosus’s regional ecosystem. How could Despegar protect its value proposition in an AI-mediated travel landscape? And to what extent should it partner with frontier AI players such as OpenAI without undermining its strategic position?
- January 2026
- Case
Despegar: Will AI Power the Next Takeoff?

By: Antonio Moreno and Karina Souza

In August 2025, Despegar, Latin America’s leading online travel agency, was closely monitoring AI’s development and its impact. As agentic AI threatened to reshape travel distribution by potentially bypassing intermediaries, Despegar’s CEO, Damián Scokin, faced a strategic tension: whether to accelerate AI adoption to remain competitive while...
- 2026
- Working Paper
Judging the Problem: A Problem-Centric Approach to Early-Stage Venture Evaluations

By: Jacqueline N. Lane and Miaomiao Zhang

Evaluating early-stage ventures requires experts to assess uncertain opportunities across multiple criteria. This paper examines how emphasizing the customer problem—whether a venture addresses a compelling problem for a clearly defined customer—affects evaluations across professional backgrounds. We conducted a randomized controlled trial at a premier venture competition where 178 judges evaluated 109 ventures (1,153 venture-judge assessments). Our intervention asked judges to prioritize the problem and customer criterion. Treatment effects varied across professional backgrounds: Compared to controls, treated investors rated business model 11% lower, whereas treated entrepreneurs rated impact 12% higher. Domain experts scrutinized problem and customer definition more critically but showed no spillover to other criteria. We demonstrate through a simple intervention how experts use their profession-specific theories of value to assess entrepreneurial ideas.
- 2026
- Working Paper
Judging the Problem: A Problem-Centric Approach to Early-Stage Venture Evaluations

By: Jacqueline N. Lane and Miaomiao Zhang

Evaluating early-stage ventures requires experts to assess uncertain opportunities across multiple criteria. This paper examines how emphasizing the customer problem—whether a venture addresses a compelling problem for a clearly defined customer—affects evaluations across professional backgrounds. We conducted a randomized controlled trial at a...

About the Unit

As the world of operations has changed, so have interests and priorities within the Unit. Historically, the TOM Unit focused on manufacturing and the development of physical products. Over the past several years, we have expanded our research, course development, and course offerings to encompass new issues in information technology, supply chains, and service industries.

The field of TOM is concerned with the design, management, and improvement of operating systems and processes. As we seek to understand the challenges confronting firms competing in today's demanding environment, the focus of our work has broadened to include the multiple activities comprising a firm's "operating core":

the multi-function, multi-firm system that includes basic research, design, engineering, product and process development and production of goods and services within individual operating units;
the networks of information and material flows that tie operating units together and the systems that support these networks;
the distribution and delivery of goods and services to customers.

Recent Publications

Heterogeneous Treatment Effects in Panel Data

By: Retsef Levi, Elisabeth Paulson, Georgia Perakis and Emily Zhang

2024 |
Working Paper |
Faculty Research

We address a core problem in causal inference: estimating heterogeneous treatment effects using panel data with general treatment patterns. Many existing methods either do not utilize the potential underlying structure in panel data or have limitations in the allowable treatment patterns. In this work, we propose and evaluate a new method that first partitions observations into disjoint clusters with similar treatment effects using a regression tree, and then leverages the (assumed) low-rank structure of the panel data to estimate the average treatment effect for each cluster. Our theoretical results establish the convergence of the resulting estimates to the true treatment effects. Computation experiments with semi-synthetic data show that our method achieves superior accuracy compared to alternative approaches, using a regression tree with no more than 40 leaves. Hence, our method provides more accurate and interpretable estimates than alternative methods.

Keywords: Econometrics; AI and Machine Learning; Mathematical Methods; Analytics and Data Science

Citation

Read Now

Despegar: Will AI Power the Next Takeoff?

By: Antonio Moreno and Karina Souza

January 2026 |
Case |
Faculty Research

In August 2025, Despegar, Latin America’s leading online travel agency, was closely monitoring AI’s development and its impact. As agentic AI threatened to reshape travel distribution by potentially bypassing intermediaries, Despegar’s CEO, Damián Scokin, faced a strategic tension: whether to accelerate AI adoption to remain competitive while risking commoditization—amid the company’s recent integration into Prosus’s regional ecosystem. How could Despegar protect its value proposition in an AI-mediated travel landscape? And to what extent should it partner with frontier AI players such as OpenAI without undermining its strategic position?

Citation

Educators

Judging the Problem: A Problem-Centric Approach to Early-Stage Venture Evaluations

By: Jacqueline N. Lane and Miaomiao Zhang

2026 |
Working Paper |
Faculty Research

Evaluating early-stage ventures requires experts to assess uncertain opportunities across multiple criteria. This paper examines how emphasizing the customer problem—whether a venture addresses a compelling problem for a clearly defined customer—affects evaluations across professional backgrounds. We conducted a randomized controlled trial at a premier venture competition where 178 judges evaluated 109 ventures (1,153 venture-judge assessments). Our intervention asked judges to prioritize the problem and customer criterion. Treatment effects varied across professional backgrounds: Compared to controls, treated investors rated business model 11% lower, whereas treated entrepreneurs rated impact 12% higher. Domain experts scrutinized problem and customer definition more critically but showed no spillover to other criteria. We demonstrate through a simple intervention how experts use their profession-specific theories of value to assess entrepreneurial ideas.

Keywords: Evaluation; Expertise Heterogeneity; Theory-based Reasoning; Field Experiment; Business Startups; Customers; Entrepreneurship; Business Model

Citation

Read Now

Harley-Davidson: On the Road to Digitization

By: Antonio Moreno, Alicia Dadlani and Mel Martin

January 2026 |
Case |
Faculty Research

After five years of investment in digital transformation of its commercial and manufacturing operations, Harley-Davidson had built the most integrated view of its business in the company’s history. Yet by late 2025, that visibility exposed an uncomfortable reality: declining demand, swollen dealer inventories, persistent supply-chain cost pressures, and renewed trade barriers converging just as the organization was being asked to absorb profound change. As digital tools reshaped manufacturing, forecasting, and customer engagement, long-standing assumptions about dealer economics, operational resilience, and the pace of transformation came under strain. With leadership transition underway and external conditions deteriorating, Harley-Davidson faced a pivotal moment—not about whether to transform, but about how much change the organization could sustain, and in what sequence, without destabilizing the systems on which it depended.