The Problem

One of the most critical yet overlooked problems in statistics is distinguishing between different missing data mechanisms. For decades, researchers have simply assumed data are Missing at Random (MAR) without proper justification, leading to potentially biased analyses and incorrect conclusions.

Our Solution

Project Lacuna uses transformer-based neural networks with attention mechanisms to analyze patterns in missing data and provide quantified assessments of missingness mechanisms. Instead of handwaving the distinction, we provide evidence-based methodology.

Technical Approach

Our methodology combines several innovative elements:

• Massive Synthetic Training Data: Generate datasets with known missingness mechanisms
• BERT-style Architecture: Encoder with binary classifier head for mechanism detection
• Attention Mechanisms: Understand which patterns drive missingness predictions
• Statistical Rigor: Three-stage validation with early stopping and uncertainty quantification

Impact Areas

Project Lacuna provides revolutionary capabilities for:

• Pharmaceutical Research: Proper analysis of clinical trial dropouts
• Biostatistics: Evidence-based missingness assessment in medical research
• Financial Modeling: Understanding missing data in economic datasets
• Insurance Analytics: Proper handling of incomplete claims data
• Academic Research: Rigorous missing data analysis across disciplines

Current Status

Design Philosophy

The Lacuna logo embodies our approach: vowels (A, U, A) appear as white text on black backgrounds, representing missing data cells in a dataset, while consonants (L, C, N) remain visible as observed data. This creates an immediate visual metaphor for missing data analysis.