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- All HBS Web
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- People (2)
- News (230)
- Research (1,212)
- Events (29)
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- 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.)
- 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.
- 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.)
- 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.
- 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–June 2022
- Other Article
Commentary on 'Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters'
There has been a substantial discussion in various methodological and applied literatures around causal inference; especially in the use of machine learning and statistical models to understand heterogeneity in treatment effects and to make optimal decision... View Details
Keywords: Causal Inference; Treatment Effect Estimation; Treatment Assignment Policy; Human-in-the-loop; Decision Making; Fairness
McFowland III, Edward. "Commentary on 'Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters'." INFORMS Journal on Data Science 1, no. 1 (April–June 2022): 21–22.
- 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.
- 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.
- Article
Distance and Political Boundaries: Estimating Border Effects under Inequality Constraints
By: Fernando Borraz, Alberto Cavallo, Roberto Rigobon and Leandro Zipitria
The border effects literature finds that political boundaries have a large impact on relative prices across locations. In this paper we show that the standard empirical specification suffers from selection bias, and propose a new methodology based on binned-quantile... View Details
Borraz, Fernando, Alberto Cavallo, Roberto Rigobon, and Leandro Zipitria. "Distance and Political Boundaries: Estimating Border Effects under Inequality Constraints." International Journal of Finance & Economics 21, no. 1 (January 2016): 3–35.
- 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.
- 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.
- Article
Estimating the Effects of Large Shareholders Using a Geographic Instrument
Large shareholders may play an important role for firm performance and policies, but identifying this empirically presents a challenge due to the endogeneity of ownership structures. We develop and test an empirical framework, which allows us to separate selection from... View Details
Keywords: Business and Shareholder Relations; Performance; Policy; Ownership; Selection and Staffing; Business Headquarters; Geography; Framework
Becker, Bo, Henrik Cronqvist, and Rudiger Fahlenbrach. "Estimating the Effects of Large Shareholders Using a Geographic Instrument ." Journal of Financial and Quantitative Analysis 46, no. 4 (August 2011): 907–942.
- 2009
- Working Paper
Estimating the Effects of Large Shareholders Using a Geographic Instrument
By: Bo Becker, Henrik Cronqvist and Rudiger Fahlenbrach
Large shareholders may play an important role for firm performance and policies, but identifying this empirically presents a challenge due to the endogeneity of ownership structures. We develop and test an empirical framework which allows us to separate selection from... View Details
Keywords: Business Headquarters; Geographic Location; Corporate Governance; Governance Controls; Performance Effectiveness; Business and Shareholder Relations; Mathematical Methods
Becker, Bo, Henrik Cronqvist, and Rudiger Fahlenbrach. "Estimating the Effects of Large Shareholders Using a Geographic Instrument." Harvard Business School Working Paper, No. 10-028, October 2009. (Revised February 2010.)
- 1999
- Working Paper
Estimating the Performance Effects of Networks in Emerging Markets
By: Tarun Khanna and Jan Rivkin
A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects
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... View Details
- 1 Jan 1999
- Conference Presentation
Estimating the Performance Effects of Networks in Emerging Markets
By: Tarun Khanna and J. Rivkin
Khanna, Tarun, and J. Rivkin. "Estimating the Performance Effects of Networks in Emerging Markets." January 1, 1999 (Winner of Academy of Management. Business Policy and Strategy Division. Best Paper Award presented by Academy of Management.)
- January 2001
- Article
Estimating the Performance Effects of Business Groups in Emerging Markets
By: Tarun Khanna and J. Rivkin
Khanna, Tarun, and J. Rivkin. "Estimating the Performance Effects of Business Groups in Emerging Markets." Strategic Management Journal 22, no. 1 (January 2001): 45–74. (Winner of Academy of Management. Business Policy and Strategy Division. Best Paper Award presented by Academy of Management.)
- 29 Oct 2009
- Working Paper Summaries
Estimating the Effects of Large Shareholders Using a Geographic Instrument
- March 2022
- Article
Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models
By: Fiammetta Menchetti and Iavor Bojinov
Researchers regularly use synthetic control methods for estimating causal effects when a sub-set of units receive a single persistent treatment, and the rest are unaffected by the change. In many applications, however, units not assigned to treatment are nevertheless... View Details
Keywords: Causal Inference; Partial Interference; Synthetic Controls; Bayesian Structural Time Series; Mathematical Methods
Menchetti, Fiammetta, and Iavor Bojinov. "Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models." Annals of Applied Statistics 16, no. 1 (March 2022): 414–435.