Causal Discovery and Missing Value Imputation via Graph Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning models are agnostic to causality, and existing imputation methods do not consider causal relationships between input variables, making them ineffective in handling missing data and discovering causal relationships, especially in real-world scenarios with partial observations.
Innovation Solution
A computer-implemented method using a graph neural network (GNN) to encode input variables into latent vectors and determine causal relationships by tuning edge probabilities, allowing for simultaneous missing value imputation and causal discovery, even with partially observed data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing imputation methods are used, then missing values can be filled, but causal relationships between variables are not considered, reducing imputation accuracy
Solution Approach 1:
The patent combines missing value imputation and causal discovery into a single unified neural network framework. The GNN simultaneously performs both tasks by integrating the imputation objective with causal relationship learning, allowing the model to leverage causal structures to improve imputation accuracy while discovering causal relationships from the same data.
Solution Approach 2:
The patent introduces latent variables as intermediaries between observed variables and imputation targets. These latent variables capture unobserved factors that influence both the missing values and causal relationships, enabling the model to account for hidden confounders and improve imputation accuracy by considering indirect causal pathways.
2Productivity
If causal relationships are discovered from partially observed data, then scalability is improved, but data completeness is reduced
Solution Approach 1:
The patent employs self-supervised learning where the model uses the partially observed data itself to train and validate the causal discovery process. The network learns to predict missing values while simultaneously discovering causal relationships, using the available data patterns to inform both tasks without requiring external complete datasets for training.
Solution Approach 2:
The patent transforms the causal discovery problem into a parameter estimation problem by representing causal relationships as learnable parameters within the neural network. This allows the model to discover causal structures by optimizing parameters during training, enabling scalable causal discovery from partial observations through standard gradient-based optimization methods.
Data Source
AI summary
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