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(695)
- People (2)
- News (109)
- Research (525)
- Events (4)
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- Faculty Publications (286)
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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.)
- 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.
- 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.
- 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.
- Forthcoming
- 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 (forthcoming). (Pre-published online July 8, 2024.)
- 2022
- Working Paper
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." Working Paper, March 2022.
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
- 2023
- Working Paper
Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach
By: Ta-Wei Huang and Eva Ascarza
Data-driven targeted interventions have become a powerful tool for organizations to optimize business outcomes
by utilizing individual-level data from experiments. A key element of this process is the estimation
of Conditional Average Treatment Effects (CATE), which... View Details
Huang, Ta-Wei, and Eva Ascarza. "Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach." Harvard Business School Working Paper, No. 24-034, December 2023.
- January 2007
- Article
A Randomized Clinical Trial of EMDR, Fluoxetine and Pill Placebo in the Treatment of PTSD: Treatment Effects and Long-term Maintenance
By: Bessel A. van der Kolk, Joseph Spinazzola, Margaret E. Blaustein, James Hopper, Elizabeth Hopper, Deborah Korn and William B. Simpson
van der Kolk, Bessel A., Joseph Spinazzola, Margaret E. Blaustein, James Hopper, Elizabeth Hopper, Deborah Korn, and William B. Simpson. "A Randomized Clinical Trial of EMDR, Fluoxetine and Pill Placebo in the Treatment of PTSD: Treatment Effects and Long-term Maintenance." Journal of Clinical Psychiatry 68, no. 1 (January 2007).
- 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.
- 05 Mar 2012
- News
It's Manufacturing's Turn for Special Treatment
- 2016
- Chapter
Changing Behavior Beyond the Here and Now
By: Todd Rogers and Erin Frey
Rogers, Todd, and Erin Frey. "Changing Behavior Beyond the Here and Now." In The Wiley Blackwell Handbook of Judgment and Decision Making, edited by Gideon Keren and George Wu, 725–748. Wiley-Blackwell, 2016.
- 2021
- Working Paper
How Much Should We Trust Staggered Difference-In-Differences Estimates?
By: Andrew C. Baker, David F. Larcker and Charles C.Y. Wang
Difference-in-differences analysis with staggered treatment timing is frequently used to assess the impact of policy changes on corporate outcomes in academic research. However, recent advances in econometric theory show that such designs are likely to be biased in the... View Details
Keywords: Difference In Differences; Staggered Difference-in-differences Designs; Generalized Difference-in-differences; Dynamic Treatment Effects; Mathematical Methods
Baker, Andrew C., David F. Larcker, and Charles C.Y. Wang. "How Much Should We Trust Staggered Difference-In-Differences Estimates?" European Corporate Governance Institute Finance Working Paper, No. 736/2021, February 2021. (Harvard Business School Working Paper, No. 21-112, April 2021.)
- 2024
- Working Paper
Antibiotic Treatments for Ulcers--Eradicating H-Pylori Infections: Case Histories of Transformational Advances
By: Amar Bhidé, Srikant M. Datar and Katherine Stebbins
This case history describes how a chance discovery of bacteria that infect stomach linings completely changed how physicians treat ulcers. Specifically, we chronicle how: 1) two Australian physicians brought the bacterial infection to the world’s attention and... View Details
Keywords: Health Care and Treatment; Technological Innovation; Innovation Strategy; Technology Adoption; Collaborative Innovation and Invention; Innovation and Invention; Governing Rules, Regulations, and Reforms
Bhidé, Amar, Srikant M. Datar, and Katherine Stebbins. "Antibiotic Treatments for Ulcers--Eradicating H-Pylori Infections: Case Histories of Transformational Advances." Harvard Business School Working Paper, No. 20-006, July 2019. (Revised May 2024.)
- 07 Oct 2014
- News
Network Effect
after, because more genes with which to work would mean more avenues to explore in new drug development, exponentially increasing the chances of finding effective treatment and prevention. In 2004, the three... View Details
Keywords: Linda Kush
- 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.