TabSODA: Tabular Diffusion based Imputation with Skip Pattern Detection and Ordinal Awareness
arXiv:2606.05361v1 Announce Type: cross Abstract: Missing data imputation in large-scale surveys faces two challenges that are not well handled by current tabular diffusion methods. First, \emph{structural skips}, cells made inapplicable by questionnaire design, should not be imputed but are often conflated with item nonresponse. Second, \emph{ordinal} responses encode ordered categories, yet most pipelines treat them as nominal levels through one-hot or analog-bit encodings. We introduce \textbf{TabSODA} (\textbf{Tab}ular diffusion with \textbf{S}kip pattern detection and \textbf{O}r\textbf{d
The proliferation of advanced AI models highlights the critical need for more robust and nuanced data imputation techniques, especially for large and complex datasets typical in surveys.
Improved missing data imputation directly enhances the reliability and interpretability of AI analyses, crucial for applications across various sectors from public policy to market research.
This research provides a more sophisticated approach to handling missing data in tabular diffusion models, addressing common pitfalls like structural skips and ordinal data representation.
- · AI researchers
- · Survey data analysts
- · Machine learning developers
- · Public policy organizations
- · Traditional imputation methods
- · Manual data cleaning
- · Analysts using less robust imputations
More accurate and reliable AI models can be built from incomplete tabular datasets.
Reduced incidence of biased or flawed conclusions in studies relying on survey data with missing values.
Accelerated development of AI applications in fields like social science and economics where large-scale survey data is prevalent.
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Read at arXiv cs.LG