Neural Network Imputation for Missing Not At Random Data
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Solution Overview
Problem
Existing machine learning models fail to accurately handle Missing Not At Random (MNAR) data, leading to biased imputation and compromised performance in data analysis tasks, as they do not account for the complex relationships between missing values and observed data.
Innovation Solution
A computer-implemented method using a series of neural networks to encode and decode data, with a loss function optimization process that minimizes differences between latent vectors and ground truth data, to determine mask vectors and impute missing values, effectively addressing MNAR scenarios by tuning parameters to account for relationships between missing and observed variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing machine learning models are used for imputing missing values, then the imputation process is simple and fast, but the imputation results are biased and inaccurate when dealing with MNAR data
Solution Approach 1:
The model segments the imputation task into multiple specialized neural networks: a first neural network for encoding data into latent vectors, a second neural network for decoding latent vectors into imputed values, and a third neural network for generating mask vectors. This segmentation allows each component to specialize in handling specific aspects of MNAR data, improving overall imputation accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The model introduces a latent vector dimension as an intermediate representation between the input data and imputed values. By transforming the imputation problem into a multi-dimensional space involving data vectors, latent vectors, and mask vectors, the model captures complex relationships in MNAR data that cannot be represented in the original feature space, thereby improving imputation precision.
2Measurement precision
If complex neural network models with multiple parameters are used to handle MNAR relationships, then imputation accuracy improves, but model identifiability becomes difficult to guarantee
Solution Approach 1:
The model performs preliminary encoding of the input data into latent vectors before generating imputed values. This preliminary transformation establishes a structured representation that captures the underlying data distribution and MNAR relationships, making the subsequent imputation process more reliable and the model parameters more identifiable through the systematic flow from encoding to decoding.
Solution Approach 2:
The model incorporates a feedback mechanism where the third neural network generates mask vectors that indicate which values are missing, and this mask information is fed back into the encoding process. This feedback loop allows the model to iteratively refine its understanding of the MNAR data structure, improving both imputation accuracy and parameter identifiability by continuously adjusting to the observed missingness patterns.
3Measurement precision
If the model accounts for complex relationships between missing and observed variables, then unbiased imputation is achieved, but the computational complexity and training difficulty increase
Solution Approach 1:
The complex task of modeling MNAR relationships is segmented across three specialized neural networks, each handling a specific function: encoding, decoding, and mask generation. This segmentation distributes the computational complexity across multiple simpler components rather than requiring one highly complex network, making training more manageable while still capturing complex MNAR relationships through the coordinated work of all components.
Solution Approach 2:
The latent vectors serve as an intermediary representation that mediates between the observed data and the imputed values. This intermediary layer simplifies the computational task by providing a compressed, structured representation of the data that captures essential relationships, reducing the direct computational complexity between input data and output imputations while maintaining the ability to model complex MNAR patterns.
Data Source
AI summary
A computer-implemented method comprising: tuning first, second and third set of parameters of respective neural networks by minimising a loss function, the loss function comprising a sum of: a measure of difference between a distribution of a first plurality of latent vectors and a distribution of a second plurality of latent vectors; and an error determined based on a set of computed vectors, a set of computed mask vectors and ground truth data for the set of data.