Many research questions in Earth and environmental sciences are inherently causal,
requiring robust analyses to establish whether and how changes in one variable cause …

When assessing causal effects, determining the target population to which the results are
intended to generalize is a critical decision. Randomized and observational studies each …

The supplementary material contains details on treatment effect estimation performed
separately on RCT data (Section A) and on observational data (Section B), derivations of the …

Click data collected by modern recommendation systems are an important source of
observational data that can be utilized to train learning-to-rank (LTR) systems. However …

Covariate adjustment is a commonly used method for total causal effect estimation. In recent
years, graphical criteria have been developed to identify all valid adjustment sets, that is, all …

Learning about cause and effect is arguably the main goal in applied econometrics. In
practice, the validity of these causal inferences is contingent on a number of critical …

Abstract Machine learning is increasingly used to discover diagnostic and prognostic
biomarkers from high-dimensional molecular data. However, a variety of factors related to …

Predictive models can fail to generalize from training to deployment environments because
of dataset shift, posing a threat to model reliability in practice. As opposed to previous …

Session-based recommendation systems (SBRs) aim to capture user preferences over time
by taking into account the sequential order of interactions within sessions. One promising …

The UK Biobank is a national prospective study of half a million participants between the
ages of 40 and 69 at the time of recruitment between 2006 and 2010, established to facilitate …