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  • August 2023
  • Article

Do Rating Agencies Behave Defensively for Higher Risk Issuers?
By: Samuel B. Bonsall IV, Kevin Koharki, Pepa Kraft, Karl A. Muller III and Anywhere Sikochi
We examine whether rating agencies act defensively toward issuers with a higher likelihood of default. We find that agencies' qualitative soft rating adjustments are more accurate as issuers' default risk grows, as evidenced by the adjustments leading to lower Type I...  View Details
Keywords: Credit Rating Agencies; Soft Rating Adjustments; Default; Credit; Performance Evaluation; Measurement and Metrics; Financial Institutions; Risk Management
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  • 2023
  • Article

Towards Bridging the Gaps between the Right to Explanation and the Right to Be Forgotten
By: Himabindu Lakkaraju, Satyapriya Krishna and Jiaqi Ma
The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications. While the right to explanation allows individuals to request an actionable explanation for an...  View Details
Keywords: Analytics and Data Science; AI and Machine Learning; Decision Making; Governing Rules, Regulations, and Reforms
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  • 2023
  • Working Paper

Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation
By: Dae Woong Ham, Michael Lindon, Martin Tingley and Iavor Bojinov
Randomized experiments have become the standard method for companies to evaluate the performance of new products or services. In addition to augmenting managers’ decision-making, experimentation mitigates risk by limiting the proportion of customers exposed to...  View Details
Keywords: Performance Evaluation; Research and Development; Analytics and Data Science; Consumer Behavior
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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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  • March–April 2023
  • Article

Market Segmentation Trees
By: Ali Aouad, Adam Elmachtoub, Kris J. Ferreira and Ryan McNellis
Problem definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision making. Methodology/results: We propose a general methodology, market segmentation trees (MSTs), for learning market...  View Details
Keywords: Decision Trees; Computational Advertising; Market Segmentation; Analytics and Data Science; E-commerce; Consumer Behavior; Marketplace Matching; Marketing Channels; Digital Marketing
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  • December 2022
  • Article

The Rise of People Analytics and the Future of Organizational Research
By: Jeff Polzer
Organizations are transforming as they adopt new technologies and use new sources of data, changing the experiences of employees and pushing organizational researchers to respond. As employees perform their daily activities, they generate vast digital data. These data,...  View Details
Keywords: Organizational Change and Adaptation; Analytics and Data Science; Technology Adoption; Employees
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  • 2023
  • Working Paper

Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development
By: Daniel Yue, Paul Hamilton and Iavor Bojinov
Predictive model development is understudied despite its centrality in modern artificial intelligence and machine learning business applications. Although prior discussions highlight advances in methods (along the dimensions of data, computing power, and algorithms)...  View Details
Keywords: Analytics and Data Science
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  • 2022
  • Article

OpenXAI: Towards a Transparent Evaluation of Model Explanations
By: Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik and Himabindu Lakkaraju
While several types of post hoc explanation methods have been proposed in recent literature, there is very little work on systematically benchmarking these methods. Here, we introduce OpenXAI, a comprehensive and extensible opensource framework for evaluating and...  View Details
Keywords: Measurement and Metrics; Analytics and Data Science
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  • December 2022
  • Article

Shaping Nascent Industries: Innovation Strategy and Regulatory Uncertainty in Personal Genomics
By: Cheng Gao and Rory McDonald
In nascent industries—whose new technologies are often poorly understood by regulators—contending with regulatory uncertainty can be crucial to organizational survival and growth. Prior research on nonmarket strategy has largely focused on established firms in mature...  View Details
Keywords: Technological Change; Innovation; Qualitative Methods; New Categories; Entrepreneurship; Technological Innovation; Governing Rules, Regulations, and Reforms; Risk and Uncertainty; Strategy
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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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  • October 2022
  • Article

How Leaders with Divergent Visions Generate Novel Strategy: Navigating the Paradox of Preservation and Modernization in Swiss Watchmaking
By: Ryan Raffaelli, Rich DeJordy and Rory M. McDonald
How do leaders with divergent visions for their organization come together to create a novel strategy? This paper employs paradox as a lens to investigate how leader-dyads can integrate opposing strategies to produce a new, generative approach. Drawing on a qualitative...  View Details
Keywords: Strategic Paradoxes; Senior Leaders; Organizational Reinvention; Leadership; Technological Innovation; Innovation and Management; Innovation Strategy; Change; Manufacturing Industry; Consumer Products Industry; Switzerland
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  • 2022
  • Article

Data Poisoning Attacks on Off-Policy Evaluation Methods
By: Elita Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin and Himabindu Lakkaraju
Off-policy Evaluation (OPE) methods are a crucial tool for evaluating policies in high-stakes domains such as healthcare, where exploration is often infeasible, unethical, or expensive. However, the extent to which such methods can be trusted under adversarial threats...  View Details
Keywords: Analytics and Data Science; Cybersecurity; Mathematical Methods
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  • June 2022
  • Article

Conservatism Gets Funded? A Field Experiment on the Role of Negative Information in Novel Project Evaluation
By: Jacqueline N. Lane, Misha Teplitskiy, Gary Gray, Hardeep Ranu, Michael Menietti, Eva C. Guinan and Karim R. Lakhani
The evaluation and selection of novel projects lies at the heart of scientific and technological innovation, and yet there are persistent concerns about bias, such as conservatism. This paper investigates the role that the format of evaluation, specifically information...  View Details
Keywords: Project Evaluation; Innovation; Knowledge Frontier; Information Sharing; Negativity Bias; Projects; Innovation and Invention; Information; Knowledge Sharing
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  • 2022
  • Article

Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.
By: Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay and Himabindu Lakkaraju
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice. Despite the growing popularity of counterfactual explanations, a...  View Details
Keywords: Machine Learning Models; Counterfactual Explanations; Adversarial Examples; Mathematical Methods
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  • 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
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  • 2022
  • Article

Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods.
By: Chirag Agarwal, Marinka Zitnik and Himabindu Lakkaraju
As Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes critical to ensure that the stakeholders understand the rationale behind their predictions. While several GNN explanation methods have been proposed recently, there has...  View Details
Keywords: Graph Neural Networks; Explanation Methods; Mathematical Methods; Framework; Theory; Analysis
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  • March 2022 (Revised April 2023)
  • Case

Hometown Foods: Changing Price Amid Inflation
By: Julian De Freitas, Jeremy Yang and Das Narayandas
During the early part of the 2021 Covid-19 pandemic, Hometown Foods, a large seller of flour-based products, thrived as consumers hoarded baked goods and took up baking to pass the time and find comfort. Then, amid growing shortages in commodities, a vaccine arrived,...  View Details
Keywords: COVID-19 Pandemic; Consumer Behavior; Supply Chain; Inflation and Deflation; Spending; Price Bubble; Price; Volatility; Food and Beverage Industry
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  • Article

Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli

An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”...  View Details

Keywords: Algorithm Bias; Personalization; Targeting; Generalized Random Forests (GRF); Discrimination; Customization and Personalization; Decision Making; Fairness; Mathematical Methods
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  • March 2022
  • Article

Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models
By: Fiammetta Menchetti and Iavor Bojinov
Researchers regularly use synthetic control methods for estimating causal effects when a sub-set of units receive a single persistent treatment, and the rest are unaffected by the change. In many applications, however, units not assigned to treatment are nevertheless...  View Details
Keywords: Causal Inference; Partial Interference; Synthetic Controls; Bayesian Structural Time Series; Mathematical Methods
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