189 citations · 202 across the 18 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 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 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…
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…
Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learning
Sunil Mucesh, William G. Hartley, Ciarán M. Gilligan-Lee +1
Understanding how galaxies form and evolve is at the heart of modern astronomy. With the advent of large-scale surveys and simulations, remarkable progress has been made in the las…
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…
Spillover Detection for Donor Selection in Synthetic Control Models
Michael O'Riordan, Ciarán M. Gilligan-Lee
Synthetic control (SC) models are widely used to estimate causal effects in settings with observational time-series data. To identify the causal effect on a target unit, SC require…
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…
Non-parametric identifiability and sensitivity analysis of synthetic control models
Jakob Zeitler, Athanasios Vlontzos, Ciaran M. Gilligan-Lee
Quantifying cause and effect relationships is an important problem in many domains. The gold standard solution is to conduct a randomised controlled trial. However, in many situati…
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…
Estimating Categorical Counterfactuals via Deep Twin Networks
Athanasios Vlontzos, Bernhard Kainz, Ciaran M. Gilligan-Lee
Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. To perform counterfactual inference, one requires knowledge of the und…
Computation in a general physical setting
Ciarán M. Gilligan-Lee
The computational abilities of theories within the generalised probabilistic theory framework has been the subject of much recent study. Such investigations aim to gain an understa…
Technology Readiness Levels for Machine Learning Systems
Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic +12
The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of dilig…
Technology Readiness Levels for AI & ML
Alexander Lavin, Gregory Renard
The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence…
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…