Technology Readiness Levels for Machine Learning Systems. - Alexander Lavin; Ciarán M. Gilligan-Lee; Alessya Visnjic; Siddha Ganju; Dava J. Newman; Sujoy Ganguly; Danny Lange; Atilim Günes Baydin; Amit Sharma 007; Adam Gibson; Yarin Gal; Eric P. Xing; et al. (CoRR, 2021)

Estimating the probabilities of causation via deep monotonic twin networks. - Athanasios Vlontzos; Bernhard Kainz; Ciarán M. Gilligan-Lee (CoRR, 2021)

D'ARTAGNAN: Counterfactual Video Generation. - Hadrien Reynaud; Athanasios Vlontzos; Mischa Dombrowski; Ciarán M. Gilligan-Lee; Arian Beqiri; Paul Leeson; Bernhard Kainz (MICCAI (8), 2022)

Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders. - Olivier Jeunen; Ciarán M. Gilligan-Lee; Rishabh Mehrotra; Mounia Lalmas (NeurIPS, 2022)

Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding. - Graham W. Van Goffrier; Lucas Maystre; Ciarán Mark Gilligan-Lee (CLeaR, 2023)

Non-parametric identifiability and sensitivity analysis of synthetic control models. - Jakob Zeitler; Athanasios Vlontzos; Ciarán Mark Gilligan-Lee (CLeaR, 2023)

Estimating categorical counterfactuals via deep twin networks. - Athanasios Vlontzos; Bernhard Kainz; Ciarán M. Gilligan-Lee (Nat. Mac. Intell., 2023)

Contrastive representations of high-dimensional, structured treatments. - Oriol Corcoll; Athanasios Vlontzos; Michael O'Riordan; Ciarán M. Gilligan-Lee (CoRR, 2024)

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; Ofer Lahav (CoRR, 2024)

Leveraging Directed Causal Discovery to Detect Latent Common Causes in Cause-Effect Pairs. - Ciarán M. Gilligan-Lee; Christopher Hart; Jonathan G. Richens; Saurabh Johri (IEEE Trans. Neural Networks Learn. Syst., 2024)

The Hardness of Validating Observational Studies with Experimental Data. - Jake Fawkes; Michael O'Riordan; Athanasios Vlontzos; Oriol Corcoll; Ciarán Mark Gilligan-Lee (AISTATS, 2025)

Local Interference: Removing Interference Bias in Semi-Parametric Causal Models. - Michael O'Riordan; Ciarán Mark Gilligan-Lee (CLeaR, 2025)