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- 2024
- Article
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
By: Michael Lingzhi Li and Kosuke Imai
A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework serves as a basis of routine experimental analyses conducted by today’s scientists across... View Details
- February 2024
- Article
Representation and Extrapolation: Evidence from Clinical Trials
By: Marcella Alsan, Maya Durvasula, Harsh Gupta, Joshua Schwartzstein and Heidi L. Williams
This article examines the consequences and causes of low enrollment of Black patients in clinical
trials. We develop a simple model of similarity-based extrapolation that predicts that evidence is
more relevant for decision-making by physicians and patients when it... View Details
- January 2024
- Case
Post-Wirecard: BaFin under Mark Branson
By: Jonas Heese, Carlota Moniz and Daniela Beyersdorfer
In November 2023, Mark Branson, the head of Germany's Federal Financial Supervisory Authority (BaFin), reflected on the efficacy of the reforms initiated since the Wirecard scandal. BaFin had been discredited after Wirecard’s downfall in 2020. The press had derided it... View Details
- November 2023
- Case
Apple Inc. in 2023
By: David B. Yoffie and Sarah von Bargen
Under CEO Tim Cook, Apple became the first trillion dollar market cap company, the first two trillion dollar company, and the first three trillion dollar company. Since the COVID pandemic, Apple gained over 20% of the world smartphone market and 50% of the U.S. market,... View Details
- 2023
- Article
On the Impact of Actionable Explanations on Social Segregation
By: Ruijiang Gao and Himabindu Lakkaraju
As predictive models seep into several real-world applications, it has become critical to ensure that individuals who are negatively impacted by the outcomes of these models are provided with a means for recourse. To this end, there has been a growing body of research... View Details
- August 2023
- Article
Anti-Corruption, Government Subsidies, and Innovation: Evidence from China
By: Lily Fang, Josh Lerner, Chaopeng Wu and Qi Zhang
We leverage an exogenous shock—the crackdown on corrupt Chinese officials beginning in 2012—and examine how the allocation of research subsidies and innovative outcomes were affected. We argue that the staggered removal of provincial heads on corruption charges during... View Details
- 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
- 2023
- Article
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means... View Details
- 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
- September 2022
- Article
Tone at the Bottom: Measuring Corporate Misconduct Risk from the Text of Employee Reviews
By: Dennis W. Campbell and Ruidi Shang
This paper examines whether information extracted via text-based statistical methods applied to employee reviews left on the website Glassdoor.com can be used to develop indicators of corporate misconduct risk. We argue that inside information on the incidence of... View Details
- Article
The Errors of Experts: When Expertise Hinders Effective Provision and Seeking of Advice and Feedback
By: Ting Zhang, Kelly Harrington and Elad Sherf
To be effective, experts need to simultaneously develop others (i.e. provide advice and feedback to novices) and advance their own learning (i.e. seek and incorporate advice and feedback from others). However, expertise, and the state of efficacy associated with it,... View Details
- Article
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability
By: Dylan Slack, Sophie Hilgard, Sameer Singh and Himabindu Lakkaraju
As black box explanations are increasingly being employed to establish model credibility in high stakes settings, it is important to ensure that these explanations are accurate and reliable. However, prior work demonstrates that explanations generated by... View Details
- Article
Learning Models for Actionable Recourse
By: Alexis Ross, Himabindu Lakkaraju and Osbert Bastani
As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations. Such explanations provide individuals adversely... View Details
- 2021
- Chapter
Towards a Unified Framework for Fair and Stable Graph Representation Learning
By: Chirag Agarwal, Himabindu Lakkaraju and Marinka Zitnik
As the representations output by Graph Neural Networks (GNNs) are increasingly employed in real-world applications, it becomes important to ensure that these representations are fair and stable. In this work, we establish a key connection between counterfactual... View Details
- June 2021
- Teaching Note
Pearson: Efficacy 2.0
By: Elie Ofek and Marco Bertini
Teaching Note for HBS Case No. 521-012. View Details
- 2021
- Article
Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring
By: Tom Sühr, Sophie Hilgard and Himabindu Lakkaraju
Ranking algorithms are being widely employed in various online hiring platforms including LinkedIn, TaskRabbit, and Fiverr. Prior research has demonstrated that ranking algorithms employed by these platforms are prone to a variety of undesirable biases, leading to the... View Details
- 2021
- Article
Fair Influence Maximization: A Welfare Optimization Approach
By: Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Max Izenberg, Ryan Brown, Eric Rice and Milind Tambe
Several behavioral, social, and public health interventions, such as suicide/HIV prevention or community preparedness against natural disasters, leverage social network information to maximize outreach. Algorithmic influence maximization techniques have been proposed... View Details
- January 2021 (Revised March 2021)
- Case
Serum Institute of India (SII): Racing to Save Lives During a Pandemic
By: Rohit Deshpandé, Anjali Raina and Rachna Chawla
The CEO of Serum Institute of India (SII), a $12.8 billion Indian Family business is faced with a risky choice between principles and profit. SII is the largest manufacturer of vaccines in the world and Adar Poonawalla, the CEO and son of the founder has to decide how... View Details
- January 2021 (Revised May 2023)
- Case
Pearson: Efficacy 2.0
By: Elie Ofek, Marco Bertini, Oded Koenigsberg and James Weber
Pearson, which billed itself as the "world's learning company," faced a host of critical decisions in mid-2020. Several years prior, it had embarked on a new path that put the learner at the heart of the business and committed to a new strategic orientation. The new... View Details
- Article
Towards Robust and Reliable Algorithmic Recourse
By: Sohini Upadhyay, Shalmali Joshi and Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan
approvals), there has been growing interest in post-hoc techniques which provide recourse to affected
individuals. These techniques generate recourses under the assumption... View Details