Causal reasoning is a crucial part of science and human intelligence. In order to discover
causal relationships from data, we need structure discovery methods. We provide a review …

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

Invariant Causal Prediction (Peters et al., 2016) is a technique for out-of-distribution
generalization which assumes that some aspects of the data distribution vary across the …

A concise and self-contained introduction to causal inference, increasingly important in data
science and machine learning. The mathematization of causality is a relatively recent …

What is the difference between a prediction that is made with a causal model and that with a
non-causal model? Suppose that we intervene on the predictor variables or change the …

An important problem in many domains is to predict how a system will respond to
interventions. This task is inherently linked to estimating the system's underlying causal …

2). For instance, in this setting with m= 10 and q= 10, IDA found 4, 4, 5, 1 and 2 true positives
for the five different networks, whereas Lasso found 1, 1, 0, 1 and 2 true positives and Elastic …

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 …

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 …