Inferring causal relations from empirical data is a central task in any scientific inquiry. To that
aim, a mathematical theory of causality has been developed, not only stating under which …

Causal influences are at the core of any empirical science, the reason why its quantification
is of paramount relevance for the mathematical theory of causality and applications …

Though the topic of causal inference is typically considered in the context of classical
statistical models, recent years have seen great interest in extending causal inference …

Recent years have seen great interest in extending causal inference concepts developed in
the context of classical statistical models to quantum theory. So far, this program has only …

Inferring causal relations from experimental observations is of primal importance in science.
Instrumental tests provide an essential tool for that aim, as they allow one to estimate causal …

Figure 1: In statistics, causal models can be used to extract cause-effect relationships from
empirical data on a complex system. Existing models, however, do not apply if at least one …

Identifying causal relations plays an indispensable role in science, economics, medicine
and everyday life. Interventionist causation associates the discovery of such relations with …

Causality is central to understanding the mechanisms of nature: some event" A" is the cause
of another event “B”. Surprisingly, causality does not follow this simple rule in quantum …

Abstract Costa and Shrapnel ([2016]) have recently proposed an interventionist theory of
quantum causation. The formalism generalizes the classical methods of Pearl ([2000]) and …

The ability to identify cause–effect relations is an essential component of the scientific
method. The identification of causal relations is generally accomplished through statistical …