Edward McFowland III

Edward McFowland III

Assistant Professor of Business Administration

Assistant Professor of Business Administration

Technology and Operations Management
+1 (617) 495-0819
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Edward McFowland III
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(617) 495-0819
Featured Work Publications Teaching Awards & Honors

Journal Articles
Journal Articles

Journal Abstracts
Journal Abstracts

  • Neill, Daniel B., Edward McFowland III, and Huanian Zheng. "Fast Subset Scan for Multivariate Spatial Biosurveillance." Emerging Health Threats Journal 4, Suppl. 1, no. s42 (2011). View Details
  • Speakman, Skyler, Edward McFowland III, and Daniel B. Neill. "Scalable Detection of Anomalous Patterns With Connectivity Constraints." Emerging Health Threats Journal 4 (2011): 11121. View Details

Book Chapters
Book Chapters

  • 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. View Details

Working Papers
Working Papers

All Publications

Edward McFowland III is an Assistant Professor in the Technology and Operations Management Unit at Harvard Business School. He teaches the first-year TOM course in the required curriculum.

Professor McFowland’s research interests – which lie at the intersection of Machine Learning, Information Systems, and Management—include the development of computationally efficient algorithms for large-scale statistical machine learning and “big data” analytics. As a data and computational social scientist, Professor McFowland aims to bridge the gap between machine learning and the social sciences (e.g., economics, public policy, and management). His work has been published in leading management, machine learning, and statistics journals, and has been supported by Adobe, Facebook, PNC Bank, AT&T Research Labs, and the National Science Foundation.

Professor McFowland earned his Ph.D. in Information Systems and Management from Carnegie Mellon University. He also holds Masters degrees in Machine Learning, Public Policy, and in Information Systems from Carnegie Mellon University. Prior to joining HBS, Professor McFowland taught at the University of Minnesota Carlson School of Management.

Featured Work
Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations
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 behavior. If the network grows according to either a latent community (stochastic block) model, or a continuous latent space model, then latent homophilous attributes can be consistently estimated from the global pattern of social ties. We show that, for common versions of those two network models, these estimates are so informative that controlling for estimated attributes allows for asymptotically unbiased and consistent estimation of social-influence effects in linear models. In particular, the bias shrinks at a rate that directly reflects how much information the network provides about the latent attributes. These are the first results on the consistent nonexperimental estimation of social-influence effects in the presence of latent homophily, and we discuss the prospects for generalizing them.
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
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 by the inclusion of those variables into an econometric framework, with the objective of estimating causal effects. Recent work highlights that, because the predictions from machine learning models are inevitably imperfect, econometric analyses based on the predicted variables are likely to suffer from bias due to measurement error. We propose a novel approach to mitigate these biases, leveraging the ensemble learning technique known as the random forest. We propose employing random forest not just for prediction, but also for generating instrumental variables to address the measurement error embedded in the prediction. The random forest algorithm performs best when comprised of a set of trees that are individually accurate in their predictions, yet which also make "different" mistakes, i.e., have weakly correlated prediction errors. A key observation is that these properties are closely related to the relevance and exclusion requirements of valid instrumental variables. We design a data-driven procedure to select tuples of individual trees from a random forest, in which one tree serves as the endogenous covariate and the other trees serve as its instruments. Simulation experiments demonstrate the efficacy of the proposed approach in mitigating estimation biases, and its superior performance over an alternative method (simulation-extrapolation), which has been suggested by prior work as a reasonable method of addressing the measurement error problem.
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 context, public or private. It relies on randomized field experiments for causal inference, machine learning for estimating heterogeneous treatment effects, and the optimization of an integer linear program for converting predictions into decisions. The net result is the discovery of individual-level targeting of policy interventions to maximize overall utility under a budget constraint. The framework is set in the context of the four pillars of analytics and is especially valuable for companies that already have an existing practice of running A/B tests. The key contribution in this work is to develop and operationalize a framework to exploit both within- and between-treatment arm heterogeneity in the utility response function, in order to derive benefits from future (optimized) prescriptions. We demonstrate the value of this framework as compared to benchmark practices (i.e., the use of the average treatment effect, uplift modeling, as well as an extension to contextual bandits) in two different settings. Unlike these standard approaches, our framework is able to recognize, adapt to, and exploit the (potential) presence of different subpopulations that experience varying costs and benefits within a treatment arm, while also exhibiting differential costs and benefits across treatment arms. As a result, we find a targeting strategy that produces an order of magnitude improvement in expected total utility, for the case where significant within- and between-treatment arm heterogeneity exists.
Journal Articles
Journal Abstracts
  • Neill, Daniel B., Edward McFowland III, and Huanian Zheng. "Fast Subset Scan for Multivariate Spatial Biosurveillance." Emerging Health Threats Journal 4, Suppl. 1, no. s42 (2011). View Details
  • Speakman, Skyler, Edward McFowland III, and Daniel B. Neill. "Scalable Detection of Anomalous Patterns With Connectivity Constraints." Emerging Health Threats Journal 4 (2011): 11121. View Details
Book Chapters
  • 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. View Details
Working Papers
Teaching
Awards & Honors
Winner of the Best Complete Paper Award at the 2022 INFORMS Workshop on Data Science for "Ensemble IV: Creating Instrumental Variables from Ensemble Learners for Robust Statistical Inference” with Gordon Burtch, Mochen Yang, and Gediminas Adomavicius.
A recipient of the 2021 Mary and Jim Lawrence Fellowship for contributions in enhancing the intellectual environment of the Carlson School of Management at University of Minnesota.
Won the Best Reviewer Award at the 2019 Conference on Information Systems and Technology.
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.
Winner of the 2016 Journal of Computational and Graphical Statistics Best Paper Award for "Penalized Fast Subset Scanning" (2016) with Skyler Speakman, Sriram Somanchi, and Daniel B. Neill.
Winner of the 2015 William W. Cooper Doctoral Dissertation Award from Carnegie Mellon University.
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