Bias Correction For Paid Search In Media Mix Modeling: Paper Review

Bias Correction For Paid Search In Media Mix Modeling: Paper Review

Bias Correction For Paid Search In Media Mix Modeling: Paper Review. This post provides a high-level overview of “Bias Correction For Paid Search In Media Mix Modeling”, providing code and implementation of key concepts.

*Bias Correction For Paid Search In Media Mix Modeling: *Linked Paper

Media Mix Modeling attempts to estimate the causal effect of media spend on sales, solely based on observational data. And, as we all know estimating causal effects from observational data is fraught with challenges.

Over time, two leading, and complimentary, frameworks have emerged for dealing with causal inference.

  1. Rubin’s Potential Outcome Framework.
  2. Pearl’s Graphical Framework.

This paper explores the use of Pearl’s graphical framework to control for selection bias in media mix modeling, specifically in paid search ads.

marketing-science machine-learning modeling causality data-science

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