Clinical decision making needs to be supported by evidence that treatments are beneficial to
individual patients. Although randomized control trials (RCTs) are the gold standard for …

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 …

Matrices of (approximate) low rank are pervasive in data science, appearing in movie
preferences, text documents, survey data, medical records, and genomics. While there is a …

The convenient access to copious multifaceted data has encouraged machine learning
researchers to reconsider correlation-based learning and embrace the opportunity of …

Does adding a theorem to a paper affect its chance of acceptance? Does labeling a post
with the author's gender affect the post popularity? This paper develops a method to …

Abstract Legislators in the European Union have long been concerned with the
environmental impact of farming activities and introduced so-called agri-environment …

Off-policy evaluation (OPE) in reinforcement learning is an important problem in settings
where experimentation is limited, such as healthcare. But, in these very same settings …

Causal analysis for time series data, in particular estimating individualized treatment effect
(ITE), is a key task in many real world applications, such as finance, retail, healthcare, etc …

The prevalence of multimorbidity has been increasing in recent years, posing a major
burden for health care delivery and service. Understanding its determinants and impact is …

Missing values challenge data analysis because many supervised and unsupervised
learning methods cannot be applied directly to incomplete data. Matrix completion based on …