189 citations · 196 across the 16 of their papers we have counts for
18 papers
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 pers…
Causal Representation Learning for Generalisable Recommendation
Yorgos Felekis, Michael O'Riordan, Oriol Corcoll +1
Predictive models trained on observational data often fail to generalise to the distributions they encounter when deployed, especially when the training data is a product of the sy…
The resource theory of causal influence and knowledge of causal influence
Marina Maciel Ansanelli, Beata Zjawin, David Schmid +5
Understanding and quantifying causal relationships between variables is essential for reasoning about the physical world. In this work, we develop a resource-theoretic framework to…
Quantum oracles give an advantage for identifying classical counterfactuals
Ciarán M. Gilligan-Lee, Yìlè Yīng, Jonathan Richens +1
We show that quantum oracles provide an advantage over classical oracles for answering classical counterfactual questions in causal models, or equivalently, for identifying unknown…
Local Interference: Removing Interference Bias in Semi-Parametric Causal Models
Michael O'Riordan, Ciarán M. Gilligan-Lee
Interference bias is a major impediment to identifying causal effects in real-world settings. For example, vaccination reduces the transmission of a virus in a population such that…