A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

Downward causation is the controversial idea that 'higher'levels of organization can causally
influence behaviour at 'lower'levels of organization. Here I propose that we can gain traction …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

This paper presents Integrated Information Theory (IIT) 4.0. IIT aims to account for the
properties of experience in physical (operational) terms. It identifies the essential properties …

We study causal representation learning, the task of inferring latent causal variables and
their causal relations from high-dimensional functions (“mixtures”) of the variables. Prior …

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 …

Identifying causal relationships and quantifying their strength from observational time series
data are key problems in disciplines dealing with complex dynamical systems such as the …

Algorithmic systems that employ machine learning play an increasing role in making
substantive decisions in modern society, ranging from online personalization to insurance …

Feature importance estimates that inform users about the degree to which given inputs
influence the output of a predictive model are crucial for understanding, validating, and …

Having insight into the causal associations in a complex system facilitates decision making,
eg, for medical treatments, urban infrastructure improvements or financial investments. The …