A graphical model is a statistical model that is associated with a graph whose nodes
correspond to variables of interest. The edges of the graph reflect allowed conditional …

Graphical models can represent a multivariate distribution in a convenient and accessible
form as a graph. Causal models can be viewed as a special class of graphical models that …

Abstract Background Personalized, precision, P4, or stratified medicine is understood as a
medical approach in which patients are stratified based on their disease subtype, risk …

There is intense interest in applying machine learning to problems of causal inference in
fields such as healthcare, economics and education. In particular, individual-level causal …

Behavioral cloning reduces policy learning to supervised learning by training a
discriminative model to predict expert actions given observations. Such discriminative …

We consider constraint-based methods for causal structure learning, such as the PC-, FCI-,
RFCI-and CCD-algorithms (Spirtes et al., 1993, 2000; Richardson, 1996; Colombo et al …

We discuss recent work for causal inference and predictive robustness in a unifying way.
The key idea relies on a notion of probabilistic invariance or stability: it opens up new …

New types of phenotyping tools generate large amounts of data on many aspects of plant
physiology and morphology with high spatial and temporal resolution. These new …

The pcalg package for R can be used for the following two purposes: Causal structure
learning and estimation of causal effects from observational data. In this document, we give …

Two leading theories within the field of suicide prevention are the interpersonal
psychological theory of suicidal behaviour (IPT) and the integrated motivational-volitional …