For a given causal question, it is important to efficiently decide which causal inference
method to use for a given dataset. This is challenging because causal methods typically rely …

Accurate predictions, as with machine learning, may not suffice to provide optimal
healthcare for every patient. Indeed, prediction can be driven by shortcuts in the data, such …

The increasing accumulation of medical data brings the hope of data-driven medical
decision-making, but its increasing complexity-as text or images in electronic health records …

In recent years, there has been a growing interest in using machine learning techniques for
the estimation of treatment effects. Most of the best-performing methods rely on …

The estimation of Conditional Average Treatment Effects (CATE) is crucial for understanding
the heterogeneity of treatment effects in clinical trials. We evaluate the performance of …

Causal analysis is fundamental to science and decision-making. It unravels the structure of
the process underlying the data and estimates the effectiveness of interventions. Deriving …

Accurate predictions, as with machine learning, may not suffice to provide optimal
healthcare for every patient. Indeed, prediction can be driven by shortcuts in the data, such …

Optimizing operational decisions, routine actions within some business or operational
process, is a key challenge across a variety of domains and application areas. The …

Real World Databases are increasingly accessible, exhaustive and with fine temporal
details. Unlike traditional data used in clinical research, they capture the routine …

Causal learning aspires to embrace the ethical concerns raised by the deployment of
predictive models in decision-making settings, as well as the growing expectations about …