189 citations · 202 across the 18 of their papers we have counts for
8 papers · 1 filter
Incremental Recommendation via Causal Models
Athanasios Vlontzos, David Gustafsson, Michael O'Riordan +1
Recommendation impressions are a finite resource, hence delivering a recommendation to a user who would discover the content organically yields no incremental value and displaces o…
Unsupervised Identification and Removal of Spurious Correlations During Fine-Tuning
Ciarán M. Gilligan-Lee, Joseph Egan, Yuchen Zhu +1
Fine-tuning a pretrained language model on a curated dataset can produce spurious correlations between the fine-tuning task and unintended latent factors---such as misaligned perso…
Causal Representation Learning for Generalisable Recommendation
Yorgos Felekis, Michael O'Riordan, Oriol Corcoll +1
Predictive models trained on observational data often fail to generalise when deployed, especially when the training data is a product of the system being optimised. Recommender sy…
The Hardness of Validating Observational Studies with Experimental Data
Jake Fawkes, Michael O'Riordan, Athanasios Vlontzos +2
Observational data is often readily available in large quantities, but can lead to biased causal effect estimates due to the presence of unobserved confounding. Recent works attemp…
Contrastive representations of high-dimensional, structured treatments
Oriol Corcoll Andreu, Athanasios Vlontzos, Michael O'Riordan +1
Estimating causal effects is vital for decision making. In standard causal effect estimation, treatments are usually binary- or continuous-valued. However, in many important real-w…
Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding
Graham Van Goffrier, Lucas Maystre, Ciarán Gilligan-Lee
Understanding and quantifying cause and effect is an important problem in many domains. The generally-agreed solution to this problem is to perform a randomised controlled trial. H…
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders
Olivier Jeunen, Ciarán M. Gilligan-Lee, Rishabh Mehrotra +1
The ability to answer causal questions is crucial in many domains, as causal inference allows one to understand the impact of interventions. In many applications, only a single int…
Leveraging directed causal discovery to detect latent common causes
Ciarán M. Lee, Christopher Hart, Jonathan G. Richens +1
The discovery of causal relationships is a fundamental problem in science and medicine. In recent years, many elegant approaches to discovering causal relationships between two var…