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- All HBS Web
(945)
- People (2)
- News (122)
- Research (723)
- Events (14)
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- Faculty Publications (394)
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- 2023
- Working Paper
Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection
By: Edward McFowland III, Sriram Somanchi and Daniel B. Neill
In the recent literature on estimating heterogeneous treatment effects, each proposed method makes its own set of restrictive assumptions about the intervention’s effects and which subpopulations to explicitly estimate. Moreover, the majority of the literature provides... View Details
Keywords: Causal Inference; Program Evaluation; Algorithms; Distributional Average Treatment Effect; Treatment Effect Subset Scan; Heterogeneous Treatment Effects
McFowland III, Edward, Sriram Somanchi, and Daniel B. Neill. "Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection." Working Paper, 2023.
- Article
A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects
By: Edward McFowland III, Sandeep Gangarapu, Ravi Bapna and Tianshu Sun
We define a prescriptive analytics framework that addresses the needs of a constrained decision-maker facing, ex ante, unknown costs and benefits of multiple policy levers. The framework is general in nature and can be deployed in any utility maximizing context, public... View Details
Keywords: Prescriptive Analytics; Heterogeneous Treatment Effects; Optimization; Observed Rank Utility Condition (OUR); Between-treatment Heterogeneity; Machine Learning; Decision Making; Analysis; Mathematical Methods
McFowland III, Edward, Sandeep Gangarapu, Ravi Bapna, and Tianshu Sun. "A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects." MIS Quarterly 45, no. 4 (December 2021): 1807–1832.
- November 2012
- Article
The Variance of Non-Parametric Treatment Effect Estimators in the Presence of Clustering
By: Samuel G. Hanson and Adi Sunderam
Non-parametric estimators of treatment effects are often applied in settings where clustering may be important. We provide a general methodology for consistently estimating the variance of a large class of non-parametric estimators, including the simple matching... View Details
Keywords: Treatment Effects; Matching Estimators; Clustering; Applications and Software; Mathematical Methods
Hanson, Samuel G., and Adi Sunderam. "The Variance of Non-Parametric Treatment Effect Estimators in the Presence of Clustering." Review of Economics and Statistics 94, no. 4 (November 2012). (Stata and Matlab Code Here.)
- 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
Imai, Kosuke, and Michael Lingzhi Li. "Experimental Evaluation of Individualized Treatment Rules." Journal of the American Statistical Association 118, no. 541 (2023): 242–256.
- 2025
- Article
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details
Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics 43, no. 1 (2025): 256–268.
- 2025
- Working Paper
Enhancing Treatment Effect Prediction on Privacy-Protected Data: An Honest Post-Processing Approach
By: Ta-Wei Huang and Eva Ascarza
As firms increasingly rely on customer data for personalization, concerns over privacy and regulatory compliance have grown. Local Differential Privacy (LDP) offers strong individual-level protection by injecting noise into data before collection. While... View Details
Keywords: Targeted Intervention; Conditional Average Treatment Effect Estimation; Differential Privacy; Honest Estimation; Post-processing; Analytics and Data Science; Consumer Behavior; Marketing
Huang, Ta-Wei, and Eva Ascarza. "Enhancing Treatment Effect Prediction on Privacy-Protected Data: An Honest Post-Processing Approach." Harvard Business School Working Paper, No. 24-034, December 2023. (Revised March 2025.)
- 2022
- Working Paper
Heterogeneity of Gain-Loss Attitudes and Expectations-Based Reference Points
By: Pol Campos-Mercade, Lorenz Goette, Thomas Graeber, Alex Kellogg and Charles Sprenger
Existing tests of reference-dependent preferences assume universal loss aversion. This paper examines heterogeneity in gain-loss attitudes, and explores its implications for identifying models of the reference point. In two experimental settings we measure gain-loss... View Details
Keywords: Reference-dependent Preferences; Rational Expectations; Personal Equilibrium; Endowment Effect; Expectations-based Reference Points
Campos-Mercade, Pol, Lorenz Goette, Thomas Graeber, Alex Kellogg, and Charles Sprenger. "Heterogeneity of Gain-Loss Attitudes and Expectations-Based Reference Points." Working Paper, August 2022.
- 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
Calonico, Sebastian, Rafael Di Tella, and Juan Cruz Lopez Del Valle. "Causal Inference During A Pandemic: Evidence on the Effectiveness of Nebulized Ibuprofen as an Unproven Treatment for COVID-19 in Argentina." NBER Working Paper Series, No. 30084, May 2022.
- Article
Treatment Of Opioid Use Disorder Among Commercially Insured U.S. Adults, 2008–17
By: Karen Shen, Eric Barrette and Leemore S. Dafny
There is abundant literature on efforts to reduce opioid prescriptions and misuse, but comparatively little on the treatment provided to people with opioid use disorder (OUD). Using claims data representing 12–15 million nonelderly adults covered through commercial... View Details
Keywords: Opioid Treatment; Medication-assisted Treatment; Substance Use Disorder; Private Insurance; Health Disorders; Health Care and Treatment; Insurance; United States
Shen, Karen, Eric Barrette, and Leemore S. Dafny. "Treatment Of Opioid Use Disorder Among Commercially Insured U.S. Adults, 2008–17." Health Affairs 39, no. 6 (June 2020): 993–1001.
- April 2021
- Article
Evaluating Firm-Level Expected-Return Proxies: Implications for Estimating Treatment Effects
By: Charles M.C. Lee, Eric C. So and Charles C.Y. Wang
We introduce a parsimonious framework for choosing among alternative expected-return proxies (ERPs) when estimating treatment effects. By comparing ERPs’ measurement-error variances in the cross section and in time series, we provide new evidence on the relative... View Details
Keywords: Implied Cost Of Capital; Expected Returns; Cost of Capital; Investment Return; Performance Evaluation
Lee, Charles M.C., Eric C. So, and Charles C.Y. Wang. "Evaluating Firm-Level Expected-Return Proxies: Implications for Estimating Treatment Effects." Review of Financial Studies 34, no. 4 (April 2021): 1907–1951.
- April 2020
- Article
Designs for Estimating the Treatment Effect in Networks with Interference
By: Ravi Jagadeesan, Natesh S. Pillai and Alexander Volfovsky
In this paper, we introduce new, easily implementable designs for drawing causal inference from randomized experiments on networks with interference. Inspired by the idea of matching in observational studies, we introduce the notion of considering a treatment... View Details
Keywords: Experimental Design; Network Inference; Neyman Estimator; Symmetric Interference Model; Homophily
Jagadeesan, Ravi, Natesh S. Pillai, and Alexander Volfovsky. "Designs for Estimating the Treatment Effect in Networks with Interference." Annals of Statistics 48, no. 2 (April 2020): 679–712.
- Article
Applying Random Coefficient Models to Strategy Research: Identifying and Exploring Firm Heterogeneous Effects
By: Juan Alcácer, Wilbur Chung, Ashton Hawk and Gonçalo Pacheco-de-Almeida
Strategy aims at understanding the differential effects of firms’ actions on performance. However, standard regression models estimate only the average effects of these actions across firms. Our paper discusses how random coefficient models (RCMs) may generate new... View Details
Alcácer, Juan, Wilbur Chung, Ashton Hawk, and Gonçalo Pacheco-de-Almeida. "Applying Random Coefficient Models to Strategy Research: Identifying and Exploring Firm Heterogeneous Effects." Strategy Science 3, no. 3 (September 2018): 481–553.
- September 2008
- Article
Firm Heterogeneity and Credit Risk Diversification
By: Samuel G. Hanson, M. Hashem Pesaran and Til Schuermann
This paper examines the impact of neglected heterogeneity on credit risk. We show that neglecting heterogeneity in firm returns and/or default thresholds leads to under estimation of expected losses (EL), and its effect on portfolio risk is ambiguous. Once EL is... View Details
Keywords: Volatility; Credit; Investment Return; Outcome or Result; Risk and Uncertainty; Loss; Diversification; Complexity; United States
Hanson, Samuel G., M. Hashem Pesaran, and Til Schuermann. "Firm Heterogeneity and Credit Risk Diversification." Journal of Empirical Finance 15, no. 4 (September 2008): 583–612.
- 23 Nov 2010
- Working Paper Summaries
Growth Through Heterogeneous Innovations
- February 2018
- Article
Retention Futility: Targeting High-Risk Customers Might Be Ineffective.
By: Eva Ascarza
Companies in a variety of sectors are increasingly managing customer churn proactively, generally by detecting customers at the highest risk of churning and targeting retention efforts towards them. While there is a vast literature on developing churn prediction models... View Details
Keywords: Retention/churn; Proactive Churn Management; Field Experiments; Heterogeneous Treatment Effect; Machine Learning; Customer Relationship Management; Risk Management
Ascarza, Eva. "Retention Futility: Targeting High-Risk Customers Might Be Ineffective." Journal of Marketing Research (JMR) 55, no. 1 (February 2018): 80–98.
- September–October 2013
- Article
The Dynamic Advertising Effect of Collegiate Athletics
By: Doug J. Chung
I measure the spillover effect of intercollegiate athletics on the quantity and quality of applicants to institutions of higher education in the United States, popularly known as the "Flutie Effect." I treat athletic success as a stock of goodwill that decays over... View Details
Keywords: Choice Modeling; Entertainment Marketing; Heterogeneity; Panel Data; Structural Modeling; Rights; Analytics and Data Science; Higher Education; Ethics; Consumer Behavior; Advertising; Sports; Advertising Industry; Education Industry
Chung, Doug J. "The Dynamic Advertising Effect of Collegiate Athletics." Marketing Science 32, no. 5 (September–October 2013): 679–698. (Lead article. Featured in HBS Working Knowledge.)
- 2020
- Working Paper
Socioeconomic Network Heterogeneity and Pandemic Policy Response
By: Abhishek Nagaraj, Mohammad Akbarpour, Cody Cook, Aude Marzuoli, Simon Mongey, Matteo Saccarola, Pietro Tebaldi, Shoshana Vasserman and Hanbin Yang
We develop and implement a heterogeneous-agents network-based empirical model to analyze alternative policies during a pandemic outbreak. We combine several data sources, including information on individuals’ mobility and encounters across metropolitan areas,... View Details
Nagaraj, Abhishek, Mohammad Akbarpour, Cody Cook, Aude Marzuoli, Simon Mongey, Matteo Saccarola, Pietro Tebaldi, Shoshana Vasserman, and Hanbin Yang. "Socioeconomic Network Heterogeneity and Pandemic Policy Response." Working Paper, June 2020.
- July–August 2024
- Article
Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals
By: Ta-Wei Huang and Eva Ascarza
Firms are increasingly interested in developing targeted interventions for customers with the best response,
which requires identifying differences in customer sensitivity, typically through the conditional average treatment
effect (CATE) estimation. In theory, to... View Details
Keywords: Long-run Targeting; Heterogeneous Treatment Effect; Statistical Surrogacy; Customer Churn; Field Experiments; Consumer Behavior; Customer Focus and Relationships; AI and Machine Learning; Marketing Strategy
Huang, Ta-Wei, and Eva Ascarza. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals." Marketing Science 43, no. 4 (July–August 2024): 863–884.
- March–April 2023
- Article
Pricing for Heterogeneous Products: Analytics for Ticket Reselling
By: Michael Alley, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li and Georgia Perakis
Problem definition: We present a data-driven study of the secondary ticket market. In particular, we are primarily concerned with accurately estimating price sensitivity for listed tickets. In this setting, there are many issues including endogeneity, heterogeneity in... View Details
Keywords: Price; Demand and Consumers; AI and Machine Learning; Investment Return; Entertainment and Recreation Industry; Sports Industry
Alley, Michael, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li, and Georgia Perakis. "Pricing for Heterogeneous Products: Analytics for Ticket Reselling." Manufacturing & Service Operations Management 25, no. 2 (March–April 2023): 409–426.
- 2024
- Working Paper
Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization
This paper introduces Incrementality Representation Learning (IRL), a novel multitask representation learning framework that predicts heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets... View Details
Keywords: Heterogeneous Treatment Effect; Multi-task Learning; Representation Learning; Personalization; Promotion; Deep Learning; Field Experiments; Customer Focus and Relationships; Customization and Personalization
Huang, Ta-Wei, Eva Ascarza, and Ayelet Israeli. "Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization." Harvard Business School Working Paper, No. 24-076, June 2024.