Robust Causal Discovery

Descrizione del progetto

Rendere più affidabile la scoperta causale

L’esplosione dei dati nell’ultimo decennio ha alimentato i progressi in settori quali la scienza dei dati, la statistica e l’econometria. Mentre i metodi tradizionali si sono concentrati sulla ricerca di modelli e associazioni, i ricercatori riconoscono ora il potere di scoprire relazioni causali per ottenere informazioni più approfondite. Questo cambiamento ha determinato un’impennata nella ricerca sull’inferenza causale. I metodi attuali, tuttavia, presuppongono che i dati reali siano puliti e perfettamente strutturati (un presupposto spesso violato da errori di misurazione e anomalie). Il progetto ROCDISCO, sostenuto dal programma di azioni Marie Skłodowska-Curie, si propone di sviluppare metodi di scoperta causale robusti che rimangano affidabili nonostante la contaminazione dei dati. Attraverso la creazione di un quadro teorico, la progettazione di tecniche comprovatamente robuste e la sperimentazione di applicazioni nel mondo reale, il progetto rafforzerà la scoperta causale e migliorerà l’affidabilità scientifica.

Obiettivo

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.