CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution

@article{Yao2022CausalMTAET,
  title={CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution},
  author={Di Yao and Chang Gong and Lei Zhang and Sheng Chen and Jingping Bi},
  journal={Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  year={2022}
}
  • Di Yao, Chang Gong, Jingping Bi
  • Published 21 December 2021
  • Computer Science
  • Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Multi-touch attribution (MTA), aiming to estimate the contribution of each advertisement touchpoint in conversion journeys, is essential for budget allocation and automatically advertising. Existing methods first train a model to predict the conversion probability of the advertisement journeys with historical data and calculate the attribution of each touchpoint by using the results counterfactual predictions. An assumption of these works is the conversion prediction model is unbiased. It can… 

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