1 citations · 1 across the 5 of their papers we have counts for
5 papers
A decision-theoretic framework for uncertainty quantification in epidemiological modelling
Nicholas Steyn, Freddie Bickford Smith, Cathal Mills +3
Estimating, understanding, and communicating uncertainty is fundamental to statistical epidemiology, where model-based estimates regularly inform real-world decisions. However, sou…
A general framework for probabilistic model uncertainty
Vik Shirvaikar, Stephen G. Walker, Chris Holmes
Existing approaches to model uncertainty typically either compare models using a quantitative model selection criterion or evaluate posterior model probabilities having set a prior…
A Critical Review of Causal Reasoning Benchmarks for Large Language Models
Linying Yang, Vik Shirvaikar, Oscar Clivio +1
Numerous benchmarks aim to evaluate the capabilities of Large Language Models (LLMs) for causal inference and reasoning. However, many of them can likely be solved through the retr…
Targeting relative risk heterogeneity with causal forests
Vik Shirvaikar, Andrea Storås, Xi Lin +1
The identification of heterogeneous treatment effects (HTE) across subgroups is of significant interest in clinical trial analysis. Several state-of-the-art HTE estimation methods,…
Rethinking recidivism through a causal lens
Vik Shirvaikar, Choudur Lakshminarayan
Predictive modeling of criminal recidivism, or whether people will re-offend in the future, has a long and contentious history. Modern causal inference methods allow us to move bey…