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In QFT, one aspect of relativistic causality is the principle of microcausality, which requires that observables associated with spacelike separated regions commute. But this principle is not by itself sufficient to rule out superluminal signalling, as examples of ‘impossible’ measurements demonstrate. Representations of the dynamics that respect relativity also play a necessary role in upholding relativistic causality in QFT. This talk will focus on the important role that principles of relativistic dynamics play in representations of local measurement in QFT.

The notion of causality is intimately tied to both, a transitive ordering on events, and the possibility of unrelated events. Thus, any causality structure is a partially ordered set or poset. This is the case in Lorentzian spacetime, which possesses a single time direction. In causal set quantum gravity, this spacetime causality structure is "first quantised" by discretising it. However, as with any dynamical quantum theory of spacetime, background notions of causality are insufficient. I will discuss how ordering and discreteness, as manifested in the sequential growth paradigm, provide a broad framework for quantum dynamical notions of causality.

There are several non-causal effects that have been attributed to quantum physics. These include the analogues of "closed timelike curve effects" in quantum circuits proposed by David Deutsch (D-CTC), and the "impossible measurements" in relativistic quantum field theory discussed by Raphael Sorkin. Based on previous work, it will be pointed out in the talk that the alleged non-causality features arise not only in quantum systems, but in the very same manner in systems that are described in the framework of classical (non-quantum) statistical mechanics or classical field theory. Therefore, although the said non-causality scenarios have been portrayed as pertaining to quantum systems or quantum fields, they are in fact not based on, nor characteristic of, the quantum nature of physical systems.

Causal reasoning is vital for effective reasoning in many domains, from healthcare to economics. In medical diagnosis, for example, a doctor aims to explain a patient’s symptoms by determining the diseases causing them. This is because causal relations, unlike correlations, allow one to reason about the consequences of possible treatments and to answer counterfactual queries. In this talk I will present some recent work done with my collaborators about how one can learn and reason with counterfactual distributions, and why this is importantly for decision making. In all cases I will strive to motivate and contextualise the results with real word examples.