32 citations · 33 across the 11 of their papers we have counts for
11 papers
Counterfactual Marginalisation: Framework for Evaluating Robustness to Nuisance Variables
Yasin Ibrahim, Hermione Warr, Robin J. Evans +1
Machine learning models can achieve strong test performance while relying on demographic or acquisition-related shortcuts. We propose counterfactual (CF) marginalisation as a test-…
MCAnalysis: An Open-Source Package for Preprocessing, Modelling, and Visualisation of Menstrual Cycle Effects in Digital Health Data
Kyra Delray, Glyn Lewis, Bola Grace +2
The menstrual cycle influences numerous physiological and psychological outcomes, yet standardised, open-source statistical methods for quantifying these cyclic effects remain lack…
Data Fusion with Distributional Equivalence Test-then-pool
Linying Yang, Xing Liu, Robin J. Evans
Randomized controlled trials (RCTs) are the gold standard for causal inference, yet practical constraints often limit the size of the concurrent control arm. Borrowing control data…
Frugal, Flexible, Faithful: Causal Data Simulation via Frengression
Linying Yang, Jens Magelund Tarp, Robin J. Evans +1
Machine learning has revitalized causal inference by combining flexible models and principled estimators, yet robust benchmarking and evaluation remain challenging with real-world…
Outcome-Informed Weighting for Robust ATE Estimation
Linying Yang, Robin J. Evans
Reliable causal effect estimation from observational data requires adjustment for confounding and sufficient overlap in covariate distributions between treatment groups. However, i…
Exact Simulation of Longitudinal Data from Marginal Structural Models
Xi Lin, Daniel de Vassimon Manela, Chase Mathis +2
Simulating longitudinal data from specified marginal structural models is a crucial but challenging task for evaluating causal inference methods and informing study design. While d…
Marginal Causal Flows for Validation and Inference
Daniel de Vassimon Manela, Laura Battaglia, Robin J. Evans
Investigating the marginal causal effect of an intervention on an outcome from complex data remains challenging due to the inflexibility of employed models and the lack of complexi…
Testing Generalizability in Causal Inference
Daniel de Vassimon Manela, Linying Yang, Robin J. Evans
Ensuring robust model performance in diverse real-world scenarios requires addressing generalizability across domains with covariate shifts. However, no formal procedure exists for…
Data fusion for efficiency gain in ATE estimation: A practical review with simulations
Xi Lin, Jens Magelund Tarp, Robin J. Evans
The integration of real-world data (RWD) and randomized controlled trials (RCT) is increasingly important for advancing causal inference in scientific research. This combination ho…
A fast score-based search algorithm for maximal ancestral graphs using entropy
Zhongyi Hu, Robin Evans
\emph{Maximal ancestral graph} (MAGs) is a class of graphical model that extend the famous \emph{directed acyclic graph} in the presence of latent confounders. Most score-based app…
Two algorithms for fitting constrained marginal models
Robin J. Evans, Antonio Forcina
We study in detail the two main algorithms which have been considered for fitting constrained marginal models to discrete data, one based on Lagrange multipliers and the other on a…