Publications

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

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

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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

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

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

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

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

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

Keywords: AI and Machine Learning; Mathematical Methods

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

Keywords: AI and Machine Learning; Mathematical Methods

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

Keywords: Analytics and Data Science; Consumer Behavior; Marketing

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

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

Keywords: Health Testing and Trials; Health Care and Treatment; Health Disorders

05 Mar 2012
News

It's Manufacturing's Turn for Special Treatment

April–June 2022
Other Article

Commentary on 'Causal Decision Making and Causal Eﬀect Estimation Are Not the Same... and Why It Matters'

By: Edward McFowland III

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

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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

SSRN

2016
Chapter

Changing Behavior Beyond the Here and Now

By: Todd Rogers and Erin Frey

Keywords: Persistence; Treatment Effects; Interventions; Behavior

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

SSRN