A kernel test for causal association via noise contrastive backdoor adjustment

A kernel test for causal association via noise contrastive backdoor adjustment

Causal inference grows increasingly complex as the dimension of confounders increases. Given treatments X𝑋, outcomes Y𝑌, and measured confounders Z𝑍, we develop a non-parametric method to test the do-null hypothesis that, after an intervention on X𝑋, there is no marginal dependence of Y𝑌 on X𝑋, agai...

書誌詳細
主要な著者:	Hu, R, Sejdinovic, D, Evans, RJ
フォーマット:	Journal article
言語:	English
出版事項:	Journal of Machine Learning Research 2024

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