Learning the Covariance of Treatment Effects Across Many Weak Experiments-Reference-Cited by-同舟云学术

Learning the Covariance of Treatment Effects Across Many Weak Experiments

Published:2024-08-24 Issue: Volume: Page:153-162
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Bibaut Aurélien¹^ORCID,Chou Winston¹^ORCID,Ejdemyr Simon¹^ORCID,Kallus Nathan²^ORCID

Affiliation:

1. Netflix, Los Gatos, CA, USA

2. Netflix & Cornell University, Los Gatos, CA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3672034

Reference20 articles.

1. Anderson, T., Kunitomo, N., Matsushita, Y., et al. The limited information maximum likelihood estimator as an angle. CIRJE No. CIRJE-F-619, CIRJE, Faculty of Economics, University of Tokyo (2009).

2. Jackknife instrumental variables estimation

3. Athey, S., Chetty, R., and Imbens, G. Combining experimental and observational data to estimate treatment effects on long term outcomes. arXiv preprint arXiv:2006.09676 (2020).

4. Athey, S., Chetty, R., Imbens, G., and Kang, H. Estimating treatment effects using multiple surrogates: The role of the surrogate score and the surrogate index. arXiv preprint arXiv:1603.09326 (2016).

5. Burgess, S., and Thompson, S. G. Interpreting findings from mendelian randomization using the mr-egger method. European journal of epidemiology 32 (2017), 377--389.

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Choosing a Proxy Metric from Past Experiments;Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining;2024-08-24