Robust Causal Discovery

Projektbeschreibung

Eine zuverlässigere Kausalerkennung bewirken

Die explosionsartige Zunahme von Daten in den letzten zehn Jahren hat zu Fortschritten in Fachbereichen wie Datenwissenschaft, Statistik und Ökonometrie geführt. Während sich die herkömmlichen Methoden auf die Suche nach Mustern und Assoziationen konzentrierten, erkennen die Forschenden nun die Möglichkeit, kausale Beziehungen aufzudecken, um tiefere Erkenntnisse zu gewinnen. Dieser Wandel hat zu einer starken Zunahme der Forschung über kausale Inferenzen geführt. Bei den derzeitigen Methoden wird allerdings davon ausgegangen, dass die Praxisdaten sauber und perfekt strukturiert sind (eine Annahme, die durch Messfehler und Anomalien häufig verletzt wird). Im Rahmen des über die Marie-Skłodowska-Curie-Maßnahmen unterstützten Projekts ROCDISCO wird das Ziel verfolgt, robuste Methoden zur Entdeckung von Kausalzusammenhängen zu erarbeiten, die auch bei kontaminierten Daten zuverlässig bleiben. Durch den Aufbau eines theoretischen Rahmens, die Entwicklung beweisbar robuster Verfahren und die Erprobung realer Anwendungen wird das Projektteam die Kausalitätserkennung stärken und die wissenschaftliche Zuverlässigkeit erhöhen.

Ziel

RObust Causal DISCOvery

Due to technological advances, the available amount of data has increased tremendously over the last decade. The fields of data science, statistics, computer science and econometrics have followed this growth as they provide indispensable tools for translating data into insights and knowledge. Where data science was traditionally concerned with learning associations in data, it has recently become clear that causal relations often provide a deeper understanding and a stronger tool in many practical applications. This has led to the flourishing of causal inference with some of the most prestigious scientific awards going to pioneers in the field over the last decade.

“Can we learn causal mechanisms from observational data?” is one of the compelling questions that is occupying scientists all over the world. Where it was originally answered by skepticism, it has become clear that we are not completely powerless and there are indeed ways to infer causal structure from observational data under the right conditions. However, all of the current methods assume that the observed data perfectly follows the underlying causal structure. Unfortunately, real world data is often contaminated by anomalies and measurement errors, violating this assumption and thus weakening the reliability of methods for causal discovery.

This proposal aims to fill this gap by developing methods for causal discovery that remain efficient and reliable under data contamination. In particular, it (i) builds a theoretical framework for robust causal discovery, (ii) develops methods for causal discovery that are provably robust and correctly identify the causal structure and (iii) investigates the effect of contamination on real-world discovery tasks. As a result, in addition to advancing the theoretical understanding of causal discovery, this proposal builds a versatile toolbox to support scientists doing causal discovery and improve the reliability of their findings.