Causal inference is a critical research topic across many domains, such as statistics,
computer science, education, public policy, and economics, for decades. Nowadays …

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 two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

Physics-informed machine learning (PIML), referring to the combination of prior knowledge
of physics, which is the high level abstraction of natural phenomenons and human …

Time series data are a collection of chronological observations which are generated by
several domains such as medical and financial fields. Over the years, different tasks such as …

Sequential recommendation holds the promise of understanding user preference by
capturing successive behavior correlations. Existing research focus on designing different …

While much attention has been given to the problem of estimating the effect of discrete
interventions from observational data, relatively little work has been done in the setting of …

Population-level modeling can define quantitative measures of individual aging by applying
machine learning to large volumes of brain images. These measures of brain age, obtained …

Estimating counterfactual outcomes over time has the potential to unlock personalized
healthcare by assisting decision-makers to answer''what-iF''questions. Existing causal …

Causality can be described in terms of a structural causal model (SCM) that carries
information on the variables of interest and their mechanistic relations. For most processes …