A Coding Guide to Survey Bias Correction Using Facebook Research Balance with IPW CBPS Ranking and Post Stratification Methods

The article discusses a comprehensive approach to correcting survey bias using the balance framework developed by Facebook Research. The primary focus is on applying various re-weighting techniques—Inverse Probability Weighting (IPW), Covariate Balancing Propensity Score (CBPS), ranking, and post-stratification—to adjust for selection biases in observational studies or surveys.

Key Points:

1. Introduction to Survey Bias:

   • Surveys often suffer from non-random sampling, leading to biased estimates.
   • The goal is to correct these biases by re-weighting the sample data so that it better represents the population.

2. The balance Framework:

   • Developed by Facebook Research, this framework provides tools for adjusting survey weights using different methods.
   • It aims to reduce bias and improve the accuracy of estimates derived from biased samples.

3. Weight Adjustment Methods:

   • IPW (Inverse Probability Weighting): Assigns weights based on the inverse probability of being included in the sample, assuming a known or estimated selection model.
   • CBPS (Covariate Balancing Propensity Score): Aims to balance covariates between treatment and control groups by estimating propensity scores that directly optimize balance.

Read the full article at MarkTechPost

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