research

2025

1. The Value of Prediction in Identifying the Worst-Off

   Unai Fischer-Abaigar, Christoph Kern, and Juan Carlos Perdomo

   In Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025

   🏆 Spotlight Poster @ ICML 2025 · Highlight Track @ FORC 2025

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   Machine learning is increasingly used in government programs to identify and support the most vulnerable individuals, prioritizing assistance for those at greatest risk over optimizing aggregate outcomes. This paper examines the welfare impacts of prediction in equity-driven contexts, and how they compare to other policy levers, such as expanding bureaucratic capacity. Through mathematical models and a real-world case study on long-term unemployment amongst German residents, we develop a comprehensive understanding of the relative effectiveness of prediction in surfacing the worst-off. Our findings provide clear analytical frameworks and practical, data-driven tools that empower policymakers to make principled decisions when designing these systems.

2. Algorithms for reliable decision-making need causal reasoning

   Christoph Kern, Unai Fischer-Abaigar, Jonas Schweisthal, Dennis Frauen, Rayid Ghani, Stefan Feuerriegel, Mihaela Schaar, and Frauke Kreuter

   Nature Computational Science, 2025

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   Decision-making inherently involves cause–effect relationships that introduce causal challenges. We argue that reliable algorithms for decision-making need to build upon causal reasoning. Addressing these causal challenges requires explicit assumptions about the underlying causal structure to ensure identifiability and estimatability, which means that the computational methods must successfully align with decision-making objectives in real-world tasks.

2024

1. Bridging the gap: Towards an expanded toolkit for AI-driven decision-making in the public sector

   Unai Fischer-Abaigar, Christoph Kern, Noam Barda, and Frauke Kreuter

   Government Information Quarterly, 2024

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   AI-driven decision-making systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health. While these systems offer great potential benefits to institutional decision-making processes, such as improved efficiency and reliability, these systems face the challenge of aligning machine learning (ML) models with the complex realities of public sector decision-making. In this paper, we examine five key challenges where misalignment can occur, including distribution shifts, label bias, the influence of past decision-making on the data side, as well as competing objectives and human-in-the-loop on the model output side. Our findings suggest that standard ML methods often rely on assumptions that do not fully account for these complexities, potentially leading to unreliable and harmful predictions. To address this, we propose a shift in modeling efforts from focusing solely on predictive accuracy to improving decision-making outcomes. We offer guidance for selecting appropriate modeling frameworks, including counterfactual prediction and policy learning, by considering how the model estimand connects to the decision-maker’s utility. Additionally, we outline technical methods that address specific challenges within each modeling approach. Finally, we argue for the importance of external input from domain experts and stakeholders to ensure that model assumptions and design choices align with real-world policy objectives, taking a step towards harmonizing AI and public sector objectives.

2. The Missing Link: Allocation Performance in Causal Machine Learning

   Unai Fischer-Abaigar, Christoph Kern, and Frauke Kreuter

   In Workshop on Humans, Algorithmic Decision-Making and Society, ICML 2024, 2024

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   Automated decision-making (ADM) systems are being deployed across a diverse range of critical problem areas such as social welfare and healthcare. Recent work highlights the importance of causal ML models in ADM systems, but implementing them in complex social environments poses significant challenges. Research on how these challenges impact the performance in specific downstream decision-making tasks is limited. Addressing this gap, we make use of a comprehensive real-world dataset of jobseekers to illustrate how the performance of a single CATE model can vary significantly across different decision-making scenarios and highlight the differential influence of challenges such as distribution shifts on predictions and allocations.

2022

1. Modeling Ordinal Mobile Data with sequential Variational Autoencoders

   Unai Fischer-Abaigar

   2022

   Master’s thesis supervised by Prof. Daniel Durstewitz

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   Ordinal data, crucial for many scientific disciplines, consists of a discrete set of labels for which a natural ordering but no specified distance measure exists. In practice, this pecu- liarity of ordinal data is oftentimes overlooked, with many models making the simplified assumption that it can be interpreted as either metric or categorical. The rise of digital tech- nologies allows the collection of ever larger data sets, facilitating the use of more powerful and expressive machine learning architectures. This thesis proposes and evaluates a deep probabilistic latent model for forecasting ordinal time series by integrating an ordered-logit model into a sequential variational autoencoder framework. The model is developed in the context of a multi-university research initiative (Living lab AI4U) with the goal to predict in- dividuals’ emotional trajectories using time series data collected from questionnaires on their smartphones to suggest personalized mental health interventions. The model is evaluated using empirical data collected during a psychiatric study and benchmark data matching this data is created to test the model’s theoretical limitations. Hierarchical parameter estimation is implemented to deal with sparse and short time series. The findings identify future av- enues for dealing with irregular time series, missing values and ways to integrate multimodal sensor data from smartphones.