Skip to Main Content
HBS Home
  • About
  • Academic Programs
  • Alumni
  • Faculty & Research
  • Baker Library
  • Giving
  • Harvard Business Review
  • Initiatives
  • News
  • Recruit
  • Map / Directions
Faculty & Research
  • Faculty
  • Research
  • Featured Topics
  • Academic Units
  • …→
  • Harvard Business School→
  • Faculty & Research→
  • Research
    • Research
    • Publications
    • Global Research Centers
    • Case Development
    • Initiatives & Projects
    • Research Services
    • Seminars & Conferences
    →
  • Publications→

Publications

Publications

Filter Results : (25) Arrow Down
Filter Results : (25) Arrow Down Arrow Up

Show Results For

  • All HBS Web  (112,695)
    • Faculty Publications  (25)

    Show Results For

    • All HBS Web  (112,695)
      • Faculty Publications  (25)

      McFowland III, Edward Remove McFowland III, Edward →

      Page 1 of 25 Results →
      • 2023
      • Working Paper

      Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality

      By: Frabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon and Karim R. Lakhani
      The public release of Large Language Models (LLMs) has sparked tremendous interest in how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. In our study conducted with Boston Consulting Group, a global management consulting firm, we...  View Details
      Keywords: Large Language Model; AI and Machine Learning; Performance Efficiency; Performance Improvement
      Citation
      SSRN
      Read Now
      Related
      Dell'Acqua, Frabrizio, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, and Karim R. Lakhani. "Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality." Harvard Business School Working Paper, No. 24-013, September 2023.
      • July 2023
      • Article

      So, Who Likes You? Evidence from a Randomized Field Experiment

      By: Ravi Bapna, Edward McFowland III, Probal Mojumder, Jui Ramaprasad and Akhmed Umyarov
      With one-third of marriages in the United States beginning online, online dating platforms have become important curators of the modern social fabric. Prior work on online dating has elicited two critical frictions in the heterosexual dating market. Women, governed by...  View Details
      Keywords: Online Dating; Internet and the Web; Analytics and Data Science; Gender; Emotions; Social and Collaborative Networks
      Citation
      Find at Harvard
      Purchase
      Related
      Bapna, Ravi, Edward McFowland III, Probal Mojumder, Jui Ramaprasad, and Akhmed Umyarov. "So, Who Likes You? Evidence from a Randomized Field Experiment." Management Science 69, no. 7 (July 2023): 3939–3957.
      • 2023
      • Working Paper

      Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness

      By: Neil Menghani, Edward McFowland III and Daniel B. Neill
      In this paper, we develop a new criterion, "insufficiently justified disparate impact" (IJDI), for assessing whether recommendations (binarized predictions) made by an algorithmic decision support tool are fair. Our novel, utility-based IJDI criterion evaluates false...  View Details
      Keywords: AI and Machine Learning; Forecasting and Prediction; Prejudice and Bias
      Citation
      Read Now
      Related
      Menghani, Neil, Edward McFowland III, and Daniel B. Neill. "Insufficiently Justified Disparate Impact: A New Criterion for Subgroup Fairness." Working Paper, June 2023.
      • 2023
      • Working Paper

      Auditing Predictive Models for Intersectional Biases

      By: Kate S. Boxer, Edward McFowland III and Daniel B. Neill
      Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we...  View Details
      Keywords: Predictive Models; Bias; AI and Machine Learning
      Citation
      Read Now
      Related
      Boxer, Kate S., Edward McFowland III, and Daniel B. Neill. "Auditing Predictive Models for Intersectional Biases." Working Paper, June 2023.
      • 2023
      • Article

      Provable Detection of Propagating Sampling Bias in Prediction Models

      By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
      With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider...  View Details
      Citation
      Read Now
      Related
      Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9562–9569. (Presented at the 37th AAAI Conference on Artificial Intelligence (2/7/23-2/14/23) in Washington, DC.)
      • 2023
      • Article

      Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators

      By: Benjamin Jakubowski, Siram Somanchi, Edward McFowland III and Daniel B. Neill
      Regression discontinuity (RD) designs are widely used to estimate causal effects in the absence of a randomized experiment. However, standard approaches to RD analysis face two significant limitations. First, they require a priori knowledge of discontinuities in...  View Details
      Keywords: Regression Discontinuity Design; Analytics and Data Science; AI and Machine Learning
      Citation
      Read Now
      Related
      Jakubowski, Benjamin, Siram Somanchi, Edward McFowland III, and Daniel B. Neill. "Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators." Journal of Machine Learning Research 24, no. 133 (2023): 1–57.
      • 2023
      • Article

      Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations.

      By: Edward McFowland III and Cosma Rohilla Shalizi
      Social influence cannot be identified from purely observational data on social networks, because such influence is generically confounded with latent homophily, that is, with a node’s network partners being informative about the node’s attributes and therefore its...  View Details
      Keywords: Causal Inference; Homophily; Social Networks; Peer Influence; Social and Collaborative Networks; Power and Influence; Mathematical Methods
      Citation
      Find at Harvard
      Purchase
      Related
      McFowland III, Edward, and Cosma Rohilla Shalizi. "Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations." Journal of the American Statistical Association 118, no. 541 (2023): 707–718.
      • October–December 2022
      • Article

      Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem

      By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
      Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed...  View Details
      Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; AI and Machine Learning; Forecasting and Prediction
      Citation
      Find at Harvard
      Register to Read
      Related
      Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
      • 2022
      • Article

      Nonparametric Subset Scanning for Detection of Heteroscedasticity

      By: Charles R. Doss and Edward McFowland III
      We propose Heteroscedastic Subset Scan (HSS), a novel method for identifying covariates that are responsible for violations of the homoscedasticity assumption in regression settings. Viewing the problem as one of anomalous pattern detection, we use subset scanning...  View Details
      Keywords: Scan Statistics; Anomaly Detection; Regression; Model Diagnostics
      Citation
      Find at Harvard
      Purchase
      Related
      Doss, Charles R., and Edward McFowland III. "Nonparametric Subset Scanning for Detection of Heteroscedasticity." Journal of Computational and Graphical Statistics 31, no. 3 (2022): 813–823.
      • 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
      Citation
      Find at Harvard
      Purchase
      Related
      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

      Pattern Detection in the Activation Space for Identifying Synthesized Content

      By: Celia Cintas, Skyler Speakman, Girmaw Abebe Tadesse, Victor Akinwande, Edward McFowland III and Komminist Weldemariam
      Generative Adversarial Networks (GANs) have recently achieved unprecedented success in photo-realistic image synthesis from low-dimensional random noise. The ability to synthesize high-quality content at a large scale brings potential risks as the generated samples may...  View Details
      Keywords: Subset Scanning; Generative Models; Synthetic Content Detection
      Citation
      Register to Read
      Related
      Cintas, Celia, Skyler Speakman, Girmaw Abebe Tadesse, Victor Akinwande, Edward McFowland III, and Komminist Weldemariam. "Pattern Detection in the Activation Space for Identifying Synthesized Content." Pattern Recognition Letters 153 (January 2022): 207–213.
      • 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
      Citation
      Find at Harvard
      Purchase
      Related
      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.
      • 2021
      • Working Paper

      Detecting Anomalous Patterns of Care Using Health Insurance Claims

      By: Sriram Somanchi, Edward McFowland III and Daniel B. Neill
      Citation
      Related
      Somanchi, Sriram, Edward McFowland III, and Daniel B. Neill. "Detecting Anomalous Patterns of Care Using Health Insurance Claims." Working Paper, 2021. (In Preparation.)
      • 2021
      • Working Paper

      Toward Automated Discovery of Novel Anomalous Patterns

      By: Edward McFowland III and Daniel B. Neill
      Citation
      Related
      McFowland III, Edward, and Daniel B. Neill. "Toward Automated Discovery of Novel Anomalous Patterns." Working Paper, 2021.
      • Article

      Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error

      By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III
      Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving...  View Details
      Keywords: Autoencoder Networks; Pattern Detection; Subset Scanning; Computer Vision; Statistical Methods And Machine Learning; Machine Learning; Deep Learning; Data Mining; Big Data; Large-scale Systems; Mathematical Methods; Analytics and Data Science
      Citation
      Read Now
      Related
      Cintas, Celia, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan, and Edward McFowland III. "Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error." Proceedings of the International Joint Conference on Artificial Intelligence 29th (2020).
      • November 2021
      • Article

      Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data

      By: William Herlands, Edward McFowland III, Andrew Gordon Wilson and Daniel B. Neill
      Identifying anomalous patterns in real-world data is essential for understanding where, when, and how systems deviate from their expected dynamics. Yet methods that separately consider the anomalousness of each individual data point have low detection power for subtle,...  View Details
      Keywords: Pattern Detection; Subset Scanning; Gaussian Processes; Mathematical Methods
      Citation
      Read Now
      Related
      Herlands, William, Edward McFowland III, Andrew Gordon Wilson, and Daniel B. Neill. "Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data." Proceedings of Machine Learning Research (PMLR) 84 (2018): 425–434. (Also presented at the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.)
      • 2018
      • Working Paper

      Efficient Discovery of Heterogeneous 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
      Citation
      Read Now
      Related
      McFowland III, Edward, Sriram Somanchi, and Daniel B. Neill. "Efficient Discovery of Heterogeneous Treatment Effects in Randomized Experiments via Anomalous Pattern Detection." Working Paper, 2018. (2nd Round Revision.)
      • 2016
      • Article

      Penalized Fast Subset Scanning

      By: Skyler Speakman, Sriram Somanchi, Edward McFowland III and Daniel B. Neill
      We present the penalized fast subset scan (PFSS), a new and general framework for scalable and accurate pattern detection. PFSS enables exact and efficient identification of the most anomalous subsets of the data, as measured by a likelihood ratio scan statistic....  View Details
      Keywords: Disease Surveillance; Likelihood Ratio Statistic; Pattern Detection; Scan Statistic; Mathematical Methods
      Citation
      Find at Harvard
      Purchase
      Related
      Speakman, Skyler, Sriram Somanchi, Edward McFowland III, and Daniel B. Neill. "Penalized Fast Subset Scanning." Journal of Computational and Graphical Statistics 25, no. 2 (2016): 382–404. (Selected for “Best of JCGS” invited session by the journal’s editor in chief.)
      • 2015
      • Article

      Scalable Detection of Anomalous Patterns With Connectivity Constraints

      By: Skyler Speakman, Edward McFowland III and Daniel B. Neill
      We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and...  View Details
      Keywords: Biosurveillance; Event Detection; Graph Mining; Scan Statistics; Spatial Scan Statistic
      Citation
      Find at Harvard
      Purchase
      Related
      Speakman, Skyler, Edward McFowland III, and Daniel B. Neill. "Scalable Detection of Anomalous Patterns With Connectivity Constraints." Journal of Computational and Graphical Statistics 24, no. 4 (2015): 1014–1033.
      • 2014
      • Chapter

      Disease Surveillance, Case Study

      By: Skyler Speakman, Sriram Somanchi, Edward McFowland III and Daniel B. Neill
      Citation
      Find at Harvard
      Purchase
      Related
      Speakman, Skyler, Sriram Somanchi, Edward McFowland III, and Daniel B. Neill. "Disease Surveillance, Case Study." In Encyclopedia of Social Network Analysis and Mining, edited by Reda Alhajj and Jon Rokne, 380–385. New York: Springer, 2014.
      • 1
      • 2
      • →
      ǁ
      Campus Map
      Harvard Business School
      Soldiers Field
      Boston, MA 02163
      →Map & Directions
      →More Contact Information
      • Make a Gift
      • Site Map
      • Jobs
      • Harvard University
      • Trademarks
      • Policies
      • Accessibility
      • Digital Accessibility
      Copyright © President & Fellows of Harvard College