Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment
outcomes including efficacy and toxicity, thereby supporting the assessment and safety of …

The use of data from randomized clinical trials to justify treatment decisions for real‐world
patients is the current state of the art. It relies on the assumption that average treatment …

We study the problem of model selection in causal inference, specifically for conditional
average treatment effect (CATE) estimation. Unlike machine learning, there is no perfect …

Predicting potential outcomes of interventions from observational data is crucial for decision-
making in medicine, but the task is challenging due to the fundamental problem of causal …

Many methods for estimating conditional average treatment effects (CATEs) can be
expressed as weighted pseudo-outcome regressions (PORs). Previous comparisons of POR …

Most studies on causal inference tackle the issue of confounding bias by reducing the
distribution shift between the control and treated groups. However, it remains an open …

Representation learning is widely used for estimating causal quantities (eg, the conditional
average treatment effect) from observational data. While existing representation learning …

Estimation of individualized treatment effects (ITE) from observational studies is a
fundamental problem in causal inference and holds significant importance across domains …

Estimating causal quantities from observational data is crucial for understanding the safety
and effectiveness of medical treatments. However, to make reliable inferences, medical …

The growing demand for personalized decision-making has led to a surge of interest in
estimating the Conditional Average Treatment Effect (CATE). Various types of CATE …