Abstract: In certain examples, a causal inference model is trained on a re-balancing task in a self-supervised manner, using ‘unlabelled’ training data pertaining to multiple domains. Rather than approaching casual inference as a domain-specific task (e.g., designing one causal-inference approach for a particular manufacturing application, another for a particular aerospace application, another for a specific medical application etc.,) a general-purpose causal inference mechanism is learned from a large, diverse training set that contains many treatments dataset over many field/applications (e.g., combining manufacturing data, engineering data, medical data etc. in a single dataset used to train a single neural network). In other words, a cross-domain causal inference model is trained, which can then be applied to a dataset in any domain, including domains that were not explicitly encountered by the neural network during training.

Abstract: A method comprising: sampling a temporal causal graph from a temporal graph distribution specifying probabilities of directed causal edges between different variables of a feature vector at a present time step, and from one variable at a preceding time step to another variables at the present time step. Based on this there are identified: a present parent which is a cause of the selected variable in the present time step, and a preceding parent which is a cause of the selected variable from the preceding time step. The method then comprises: inputting a value of each identified present and preceding parent into a respective encoder, resulting in a respective embedding of each of the present and preceding parents; combining the embeddings of the present and preceding parents, resulting in a combined embedding; inputting the combined embedding into a decoder, resulting in a reconstructed value of the selected variable.

Abstract: A method comprising: sampling a first causal graph from a first graph distribution modelling causation between variables in a feature vector, and sampling a second causal graph from a second graph distribution modelling presence of possible confounders, a confounder being an unobserved cause of both of two variables. The method further comprises: identifying a parent variable which is a cause of a selected variable according to the first causal graph, and which together with the selected variable forms a confounded pair having a respective confounder being a cause of both according to the second causal graph. A machine learning model encodes the parent to give a first embedding, and encodes information on the confounded pair give a second embedding. The embeddings are combined and then decoded to give a reconstructed value. This mechanism may be used in training the model or in treatment effect estimation.

Abstract: There is described a machine learning system comprising a first subsystem and a second subsystem remote from the first subsystem. The first subsystem comprises an environment having multiple possible states and a decision making subsystem comprising one or more agents. Each agent is arranged to receive state information indicative of a current state of the environment and to generate an action signal dependent on the received state information and a policy associated with that agent, the action signal being operable to cause a change in a state of the environment. Each agent is further arranged to generate experience data dependent on the received state information and information conveyed by the action signal. The first subsystem includes a first network interface configured to send said experience data to the second subsystem and to receive policy data from the second subsystem.

Abstract: A header for a heat exchanger includes an elongate header plate having a pair of substantially parallel long edges. The header plate includes an elongate slot extending in a first direction across the header plate and a pair of ridges extending in a second direction along the header plate. The second direction is substantially perpendicular to the first direction. Edge regions of the header plate are formed on sides of the ridges adjacent the long edges. At least one of the edge regions lie in a first plane and crests of the ridges lie in a second plane. The first plane is substantially parallel to and offset from the second plane.