Good, Fast, Cheap: How to do Data Science with Missing Data

Resources

Academic Content (roughly sorted from least technical to most)

• Good summary of single vs. multiple imputation
• UTexas Slides on Missing Data
• Flexible Imputation of Missing Data, 2nd ed.
• Pattern Submodel Paper from "Biostatistics"
• The prevention and handling of missing data
• Should you use a missing data indicator?
• Are all biases missing data problems?
• Accounting for missing data in statistical analyses: multiple imputation is not always the answer
• The proportion of missing data should not be used to guide decisions on multiple imputation
• Boston University Technical Report on Missing Data, Assumptions, and Applications
• Andrew Gelman Chapter on Missing Data - thorough and very good, but academic

Python

• Scikit-Learn Write-Up of Imputation
• Scikit-Learn SimpleImputer Class
• Scikit-Learn IterativeImputer Class note: this is experimental
• Scikit-Learn KNNImputer Class
• Scikit-Learn Mean Imputation
• MissingNo in Python
• MissingNo Paper

Rubin's Rules (combining parameter estimates across multiple imputations)

• Article about Rubin's Rules
• Rubin's Rules Formulas
• R Code for Pooling Estimates

Non-Statistical References

• How One 19-Year-Old Illinois Man Is Distorting National Polling Averages - NYTimes

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