Algorithms for Reliable Decision-Making Need Causal Reasoning

MCML Authors

Christoph Kern

Prof. Dr.

Associate

Social Data Science and AI

Jonas Schweisthal

→ Group Stefan Feuerriegel
 Artificial Intelligence in Management

Dennis Frauen

→ Group Stefan Feuerriegel
 Artificial Intelligence in Management

Stefan Feuerriegel

Prof. Dr.

Principal Investigator

Artificial Intelligence in Management

Frauke Kreuter

Prof. Dr.

Principal Investigator

Social Data Science and AI

Abstract

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. Algorithmic decision-making (ADM) has become common in a wide range of domains, including precision medicine, manufacturing, education, hiring, the public sector, and smart cities. At the core of ADM systems are data-driven models that learn from data to recommend decisions, often with the goal of maximizing a defined utility function1. For example, in smart city contexts, ADM is frequently used to optimize traffic flow through predictive models that analyze real-time data, thereby reducing congestion and improving urban mobility. Another prominent application area for ADM are normative decision support systems (often subsumed under ‘prescriptive analytics’) or, more recently, artificial intelligence (AI) agents that either inform or automatically execute managerial and operational decisions in industry. Yet, the applications of ADM to high-stakes decisions face safety and reliability issues1,2,3. Often, the objectives of ADM systems fail to align with the nuanced goals of real-world decision-making, thus creating a tension between the potential of ADM and the risk of harm and failure. Especially when deployed in dynamic, real-world environments, ADM can amplify systemic disadvantages for vulnerable communities and lead to flawed decisions. In this Comment, we argue that reliable algorithmic decision-making — systems that perform safely and robustly under deployment conditions — must be grounded in causal reasoning.

article

---

Nature Computational Science

5. May. 2025.

---

Authors

C. Kern • U. Fischer-Abaigar • J. Schweisthal • D. Frauen • R. Ghani • S. Feuerriegel • M. van der Schaar • F. Kreuter

---

Links

DOI

---

Research Areas

 A1 | Statistical Foundations & Explainability

 C4 | Computational Social Sciences