Principal Stratification for Advertising Experiments

@inproceedings{Berman2019PrincipalSF,
  title={Principal Stratification for Advertising Experiments},
  author={Ron Berman and Elea McDonnell Feit},
  year={2019}
}
  • Ron Berman, Elea McDonnell Feit
  • Published 2019
  • Mathematics
  • Advertising experiments often suffer from noisy responses making precise estimation of the average treatment effect (ATE) and evaluating ROI difficult. We develop a principal stratification model that improves the precision of the ATE by dividing the customers into three strata -- those who buy regardless of ad exposure, those who buy only if exposed to ads and those who do not buy regardless. The method decreases the variance of the ATE by separating out the typically large share of customers… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 25 REFERENCES

    The benefit of stratification in clinical trials revisited.

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Test & Roll: Profit-Maximizing A/B Tests

    VIEW 1 EXCERPT