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Evaluating the causal impacts of possible interventions is crucial for informing decision-making, especially towards improving access to opportunity. However, if causal effects are heterogeneous and predictable from covariates, personalized…
Recommender systems have become crucial in the modern digital landscape, where personalized content, products, and services are essential for enhancing user experience. This paper explores statistical models for recommender systems,…
We study the problem of optimal long term portfolio selection with a view to beat a benchmark. Two kinds of objectives are considered. One concerns the probability of outperforming the benchmark and seeks either to minimise the decay rate…
The values of software organizations are crucial for achieving high performance; in particular, agile development approaches emphasize their importance. Researchers have thus far often assumed that a specific set of values, compatible with…
We study strategic classification in binary decision-making settings where agents can modify their features in order to improve their classification outcomes. Importantly, our work considers the causal structure across different features,…
Visual statistical inference is a way to determine significance of patterns found while exploring data. It is dependent on the evaluation of a lineup, of a data plot among a sample of null plots, by human observers. Each individual is…
Resource autoscaling mechanisms in cloud environments depend on accurate performance metrics to make optimal provisioning decisions. When infrastructure faults including hardware malfunctions, network disruptions, and software anomalies…
We examine the growing gender gap in venture capital funding, focusing on accelerator programs in the U.S. We collect a unique dataset with detailed information on accelerators and startups. Using a two-stage methodology, we first estimate…
Feature importance scores are ubiquitous tools for understanding the predictions of machine learning models. However, many popular attribution methods suffer from high instability due to random sampling. Leveraging novel ideas from…
For better or for worse, rankings of institutions, such as universities, schools and hospitals, play an important role today in conveying information about relative performance. They inform policy decisions and budgets, and are often…
Predicting stock price movements is a pivotal element of investment strategy, providing insights into potential trends and market volatility. This study specifically examines the predictive capacity of historical stock prices and technical…
Large-scale matrix data has been widely discovered and continuously studied in various fields recently. Considering the multi-level factor structure and utilizing the matrix structure, we propose a multilevel matrix factor model with both…
In many cases, feature selection is often more complicated than identifying a single subset of input variables that would together explain the output. There may be interactions that depend on contextual information, i.e., variables that…
The rise of algorithmic decision-making has spawned much research on fair machine learning (ML). Financial institutions use ML for building risk scorecards that support a range of credit-related decisions. Yet, the literature on fair ML in…
Classification, a heavily-studied data-driven machine learning task, drives an increasing number of prediction systems involving critical human decisions such as loan approval and criminal risk assessment. However, classifiers often…
Yield and quality improvement is of paramount importance to any manufacturing company. One of the ways of improving yield is through discovery of the root causal factors affecting yield. We propose the use of data-driven interpretable…
Concurrent engineering taking into account product life-cycle factors seems to be one of the industrial challenges of the next years. Cost estimation and management are two main strategic tasks that imply the possibility of managing costs…
Factor analysis is a statistical technique employed to evaluate how observed variables correlate through common factors and unique variables. While it is often used to analyze price movement in the unstable stock market, it does not always…
This study uses an innovative measure, the Semantic Brand Score, to assess the interest of stakeholders in different company core values. Among others, we focus on corporate social responsibility (CSR) core value statements, and on the…
The rapid adoption of Low-Code Development Platforms (LCDPs) has created a critical need for systematic evaluation methodologies that enable organizations to make informed platform selection decisions. This paper presents a comprehensive…