Inverse problems in statistical physics are motivated by the challenges of 'big data'in
different fields, in particular high-throughput experiments in biology. In inverse problems, the …

Feature selection is a crucial preprocessing step in data analytics and machine learning.
Classical feature selection algorithms select features based on the correlations between …

An important goal common to domain adaptation and causal inference is to make accurate
predictions when the distributions for the source (or training) domain (s) and target (or test) …

This article presents an overview of several known approaches to causal discovery. It is
organized by relating the different fundamental assumptions that the methods depend on …

Causal effect identification is the task of determining whether a causal distribution is
computable from the combination of an observational distribution and substantive …

In many fields of scientific research and real-world applications, unbiased estimation of
causal effects from non-experimental data is crucial for understanding the mechanism …

Probabilistic (Bayesian) modeling has experienced a surge of applications in almost all
quantitative sciences and industrial areas. This development is driven by a combination of …

Do-calculus is concerned with estimating the interventional distribution of an action from the
observed joint probability distribution of the variables in a given causal structure. All …

Causal models are mathematical models representing causal relationships within an
individual system or population. They facilitate inferences about causal relationships from …

One common task in many data sciences applications is to answer questions about the
effect of new interventions, like:what would happen to $ Y $ if we make $ X $ equal to $ x …