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McFowland III, Edward Remove McFowland III, Edward →

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  • 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
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  • 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
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  • 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
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  • 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
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  • 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
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  • 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
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  • 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
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  • 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
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  • 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
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  • 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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  • 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
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  • 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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  • 2021
  • Working Paper

Detecting Anomalous Patterns of Care Using Health Insurance Claims
By: Sriram Somanchi, Edward McFowland III and Daniel B. Neill
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  • 2021
  • Working Paper

Toward Automated Discovery of Novel Anomalous Patterns
By: Edward McFowland III and Daniel B. Neill
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  • 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
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  • 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
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  • 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
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  • 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
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  • 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
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  • 2014
  • Chapter

Disease Surveillance, Case Study
By: Skyler Speakman, Sriram Somanchi, Edward McFowland III and Daniel B. Neill
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