CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution-Reference-Cited by-同舟云学术

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

Published:2022-08-14 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Yao Di¹,Gong Chang¹,Zhang Lei²,Chen Sheng²,Bi Jingping¹

Affiliation:

1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

2. Alibaba Inc, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539108

Reference33 articles.

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2. Ahmed M. Alaa and Mihaela van der Schaar . 2018 . Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design. In ICML'18 (Proceedings of Machine Learning Research), Vol. 80 . PMLR , 129--138. Ahmed M. Alaa and Mihaela van der Schaar. 2018. Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design. In ICML'18 (Proceedings of Machine Learning Research), Vol. 80. PMLR, 129--138.

3. Sai Kumar Arava Chen Dong Zhenyu Yan Abhishek Pani etal 2018. Deep neural net with attention for multi-channel multi-touch attribution. arXiv:1809.02230 (2018). Sai Kumar Arava Chen Dong Zhenyu Yan Abhishek Pani et al. 2018. Deep neural net with attention for multi-channel multi-touch attribution. arXiv:1809.02230 (2018).

4. Peter C Austin . 2011. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate behavioral research , Vol. 46 , 3 ( 2011 ), 399--424. Peter C Austin. 2011. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate behavioral research, Vol. 46, 3 (2011), 399--424.

5. Experimental Designs and Estimation for Online Display Advertising Attribution in Marketplaces

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