Causal Inference - Publications - Faculty & Research - Harvard Business School

Filter Results: (73)

Filter Results: (73)

Show Results For

← Page 2 of 73 Results →

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

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

Find at Harvard

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

Find at Harvard

Article

Your Visual System Provides All the Information You Need to Make Moral Judgments about Generic Visual Events

By: Julian De Freitas and George A. Alvarez

To what extent are people's moral judgments susceptible to subtle factors of which they are unaware? Here we show that we can change people’s moral judgments outside of their awareness by subtly biasing perceived causality. Specifically, we used subtle visual... View Details

Keywords: Moral Judgment; Perceived Causality; Visual Illusions; Moral Sensibility; Judgments

Find at Harvard

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

Find at Harvard

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

2021
Working Paper

Population Interference in Panel Experiments

By: Iavor I Bojinov, Kevin Wu Han and Guillaume Basse

The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit's outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in... View Details

Keywords: Finite Population; Potential Outcomes; Dynamic Causal Effects; Mathematical Methods

March 2016
Case

Evive Health and Workplace Influenza Vaccinations

By: John Beshears

Evive Health is a company that manages communication campaigns on behalf of health insurance plans and large employers. Using big data techniques and insights from behavioral economics, Evive deploys targeted and effective messages that improve individuals' health... View Details

Keywords: Vaccination; Influenza; Flu Shot; Preventive Care; Health Care; Behavioral Economics; Choice Architecture; Nudge; Experimental Design; Randomized Controlled Trial; RCT; Causal Inference; Consumer Behavior; Health Care and Treatment; Health Testing and Trials; Communication Strategy; Health Industry

March 2016 (Revised March 2022)
Teaching Note

Evive Health and Workplace Influenza Vaccinations

By: John Beshears

Evive Health is a company that manages communication campaigns on behalf of health insurance plans and large employers. Using big data techniques and insights from behavioral economics, Evive deploys targeted and effective messages that improve individuals' health... View Details

Article

Optimality Bias in Moral Judgment

By: Julian De Freitas and Samuel G.B. Johnson

We often make decisions with incomplete knowledge of their consequences. Might people nonetheless expect others to make optimal choices, despite this ignorance? Here, we show that people are sensitive to moral optimality: that people hold moral agents accountable... View Details

Keywords: Moral Judgment; Lay Decision Theory; Theory Of Mind; Causal Attribution; Moral Sensibility; Decision Making

Find at Harvard

Awards

Runner-Up for Best Paper Award, INFORMS Workshop on Data Science, 2018

By: Edward McFowland III

Runner Up for the 2018 Best Paper Award at the INFORMS Workshop on Data Science for "Using Data-Mined Variables in Causal Inference Tasks: A Random Forest Approach to the Measurement Error Problem" with Mochen Yang, Gordon Burtch, and Gediminas Adomavicius. View Details

Ta-Wei Huang

Ta-Wei (David) Huang is a PhD candidate in Quantitative Marketing at Harvard Business School. His research integrates causal inference and machine learning to address methodological challenges and unintended consequences in targeting, personalization, and online... View Details

Article

How Much Should We Trust Staggered Difference-In-Differences Estimates?

By: Andrew C. Baker, David F. Larcker and Charles C.Y. Wang

We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. These biases are likely to be relevant for a large portion of research settings in finance, accounting, and law that... View Details

Keywords: Difference In Differences; Staggered Difference-in-differences Designs; Generalized Difference-in-differences; Dynamic Treatment Effects; Mathematical Methods

Find at Harvard

2024
Working Paper

Personalization and Targeting: How to Experiment, Learn & Optimize

By: Aurelie Lemmens, Jason M.T. Roos, Sebastian Gabel, Eva Ascarza, Hernan Bruno, Elea McDonnell Feit, Brett Gordon, Ayelet Israeli, Carl F. Mela and Oded Netzer

Personalization has become the heartbeat of modern marketing. Advances in causal inference and machine learning enable companies to understand how the same marketing action can impact the choices of individual customers differently. This article provides an academic... View Details

Keywords: Personalization; Targeting; Experiments; Observational Studies; Policy Implementation; Policy Evaluation; Customization and Personalization; Marketing Strategy; AI and Machine Learning

2012
Chapter

Mental Health in the Aftermath of Conflict

By: Quy-Toan Do and Lakshmi Iyer

We survey the recent literature on the mental health effects of conflict. We highlight the methodological challenges faced in this literature, which include the lack of validated mental health scales in a survey context, the difficulties in measuring individual... View Details

Keywords: Conflict of Interests; Measurement and Metrics; Surveys; Analytics and Data Science; Ethnicity; War; Health Disorders; Body of Literature; Problems and Challenges; Bosnia and Hercegovina

2009
Working Paper

Mental Health in the Aftermath of Conflict

By: Quy-Toan Do and Lakshmi Iyer

We survey the recent literature on the mental health effects of conflict. We highlight the methodological challenges faced in this literature, which include the lack of validated mental health scales in a survey context, the difficulties in measuring individual... View Details

Keywords: Ethnicity; War; Health Disorders; Policy; Health Care and Treatment; Conflict and Resolution; Bosnia and Hercegovina

Iavor I. Bojinov

Iavor Bojinov is an Assistant Professor of Business Administration and the Richard Hodgson Fellow at Harvard Business School. He is the co-PI of the AI and Data Science Operations Lab and a faculty affiliate in the Department of Statistics at Harvard University and... View Details

December 2022
Article

Fostering Perceptions of Authenticity via Sensitive Self-Disclosure

By: Li Jiang, Leslie K. John, Reihane Boghrati and Maryam Kouchaki

Leaders’ perceived authenticity—the sense that leaders are acting in accordance with their “true self”—is associated with positive outcomes for both employees and organizations alike. How might leaders foster this impression? We show that sensitive self-disclosure, in... View Details

Keywords: Authenticity; Weaknesses; Self-disclosure; Leaders; Impression Management; Leadership Style; Motivation and Incentives

Find at Harvard

Teaching Interest

Overview

Paul is primarily interested in teaching data science to management students through the case method. This includes technical topics (programming and statistics) as well as higher-level management issues (digital transformation, data governance, etc.) As a research... View Details

Keywords: A/B Testing; AI; AI Algorithms; AI Creativity; Algorithm; Algorithm Bias; Algorithmic Bias; Algorithmic Fairness; Algorithms; Analytics; Application Program Interface; Artificial Intelligence; Causality; Causal Inference; Computing; Computers; Data Analysis; Data Analytics; Data Architecture; Data As A Service; Data Centers; Data Governance; Data Labeling; Data Management; Data Manipulation; Data Mining; Data Ownership; Data Privacy; Data Protection; Data Science; Data Science And Analytics Management; Data Scientists; Data Security; Data Sharing; Data Strategy; Data Visualization; Database; Data-driven Decision-making; Data-driven Management; Data-driven Operations; Datathon; Economics Of AI; Economics Of Innovation; Economics Of Information System; Economics Of Science; Forecast; Forecast Accuracy; Forecasting; Forecasting And Prediction; Information Technology; Machine Learning; Machine Learning Models; Prediction; Prediction Error; Predictive Analytics; Predictive Models; Analysis; AI and Machine Learning; Analytics and Data Science; Applications and Software; Digital Transformation; Information Management; Digital Strategy; Technology Adoption

December 2017
Article

Discordant vs. Harmonious Selves: The Effects of Identity Conflict and Enhancement on Sales Performance in Employee-Customer Interactions

By: Lakshmi Ramarajan, Nancy Rothbard and Steffanie Wilk

Across multiple studies, we examine how identity conflict and enhancement within people affect performance in tasks that involve interactions between people through two mechanisms: role-immersion, operationalized as intrinsic motivation, and role-taking,... View Details

Keywords: Identity; Interpersonal Communication; Sales; Performance

Find at Harvard

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

Find at Harvard

←
1
2
3
4
→