scientific-missing-data-analysis

Diagnose missing data patterns and apply multiple imputation methods.

3|1|Updated Feb 11, 2026

Search Tags:#data-quality #imputation #statistical-analysis #missing-data #mice #missing-patterns

Ready to run

npx skills add https://github.com/nahisaho/satori --skill scientific-missing-data-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scientific-missing-data-analysis
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-missing-data-analysis
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-missing-data-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

欠損データの診断・補完・感度分析を通じて、偏りのない統計推論を実現します。

Core Features & Use Cases

MCAR/MAR/MNAR の機構を判定し、データの欠損性を理解します。
MICE、KNN、MissForest、VAE/GAIN などの手法による欠損値の補完と Rubin's Rules による統合をサポートします。
欠損パターンの可視化や、パイプライン内での再現性の高い分析ワークフローを提供します。

Quick Start

Run the missing data analysis workflow on your dataset to diagnose patterns, apply imputations, and compare results.

Frequently Asked Questions about scientific-missing-data-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I diagnose missing data patterns in my dataset?▼

Diagnose missing data patterns by assessing MCAR, MAR, and MNAR mechanisms and visualizing missingness to understand data structure. This enables unbiased statistical inference by clarifying why values are absent before applying any imputation techniques.

What is the best way to impute missing values for statistical analysis?▼

Impute missing values using MICE, KNN, MissForest, or VAE/GAIN depending on your data structure. Apply Rubin's rules to integrate the results across multiple imputations, ensuring reproducible workflows and unbiased downstream statistical inference.

When do I need to assess MCAR, MAR, and MNAR mechanisms?▼

Assess MCAR, MAR, and MNAR mechanisms when your dataset has missing values that could bias statistical inference. Understanding these missing data patterns helps determine the appropriate imputation strategy and ensures reproducible analytics pipelines.

Can I use MICE and KNN imputation in a reproducible analytics pipeline?▼

Yes, you can use MICE and KNN imputation within reproducible data analytics pipelines. The workflow supports diagnosing missingness, applying multiple imputation methods, and synthesizing results with Rubin's rules to maintain clear, consistent outputs.

How does Rubin's rules work for multiple imputation results?▼

Rubin's rules integrate results from multiple imputations by combining estimates and variances to produce unbiased statistical inference. This approach ensures that uncertainty from imputed missing data is accurately reflected in the final analysis outputs.

What are the limitations of using VAE or GAIN for missing data imputation?▼

VAE and GAIN imputation may require substantial computational resources and complex tuning compared to simpler methods like KNN. Consider these limitations when diagnosing missing data patterns, ensuring the chosen method aligns with your reproducible workflow requirements.