CAUSAL DISCOVERY AND MISSING VALUE IMPUTATION

• Main Authors: PEYTON JONES, Simon Loftus, ZHANG, Cheng, LAMB, Angus James, MORALES- ÁLVAREZ, Pablo, ALLAMANIS, Miltiadis
• Format: Patent
• Language: English
• Published: 10.10.2024
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; PHYSICS

A computer-implemented method comprising: receiving an input vector comprising values of variables; using a first neural network to encode the values of the variables of the input vector into a plurality of latent vectors; determining an output vector by inputting the plurality of latent vectors into a second neural network comprising a graph neural network, wherein the graph neural network is parametrized by a graph comprising edge probabilities indicating causal relationships between the variables; and minimising a loss function by tuning the edge probabilities of the graph, at least one parameter of the first neural network and at least one parameter of the second neural network, wherein the loss function comprises a function of the graph and a measure of difference between the input vector and the output vector