A new artificial intelligence (AI) has allowed researchers at UCL and Babylon Health, for the first time, to demonstrate a useful and reliable way of sifting through masses of correlating data to spot when correlation means causation.

By fusing old, overlapping and incomplete datasets this new method, inspired by quantum cryptography, paves the way for researchers to glean the results of medical trials that would otherwise be too expensive, difficult or unethical to run. The research is being published at the prestigious and peer-reviewed Association for Advancement of Artificial Intelligence (AAAI) conference in New York.
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Dr Ciarán Lee, Senior Research Scientist at Babylon and Honorary Senior Research Associate at UCL Physics & Astronomy, explained: "Scientists have it hammered into them that correlation does not mean causation; ice-cream sales don't cause sunburn despite rates of both shooting up during the summer. To find the exact cause of sunburn we whittle down or control as many variables as possible. Then when our datasets show that a change in sun exposure matches a change in sunburn, we can be confident the sun exposure was the causative variable. The problem is the real world is rarely neat and tidy and it can be really hard to control all the variables and work out which is causative."

Scientists started looking for other ways to help spot causative variables. A theory born from physics suggests that everything becomes more disordered and complicated with time, so a cause should be less disordered and complex than its effect. Dr Lee said "If you take your dataset and give each of the variables a complexity rating you can work backwards and spot which one is the cause. But that just helps for that one dataset - we wanted to see if there was a way of combining datasets, ones with gaps or where researchers were asking different questions to what they're interested in now. That could be a game-changer."

Dr Lee was inspired by quantum cryptography. The strange laws of quantum physics mean that two users can send a message and then use a mathematical formula to prove whether someone else is eavesdropping on their conversation. Dr Lee realised that datasets could work in a similar way, but thinking of a potential causative variable from another dataset as the eavesdropper. "If one dataset shows us that obesity causes heart disease, and another shows vitamin D causes obesity we can use a mathematical formula to prove whether vitamin D causes obesity or not. This is what our AI is doing."

Pearl, 1986 wrote:ABSTRACT

Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to represent the generic knowledge of a domain expert, and it turns into a computational architecture if the links are used not merely for storing factual knowledge but also for directing and activating the data flow in the computations which manipulate this knowledge.

The first part of the paper deals with the task of fusing and propagating the impacts of new information through the networks in such a way that, when equilibrium is reached, each proposition will be assigned a measure of belief consistent with the axioms of probability theory. It is shown that if the network is singly connected (e.g. tree-structured), then probabilities can be updated by local propagation in an isomorphic network of parallel and autonomous processors and that the impact of new information can be imparted to all propositions in time proportional to the longest path in the network.

The second part of the paper deals with the problem of finding a tree-structured representation for a collection of probabilistically coupled propositions using auxiliary (dummy) variables, colloquially called "hidden causes." It is shown that if such a tree-structured representation exists, then it is possible to uniquely uncover the topology of the tree by observing pairwise dependencies among the available propositions (i.e., the leaves of the tree). The entire tree structure, including the strengths of all internal relationships, can be reconstructed in time proportional to n log n, where n is the number of leaves.