Web: http://arxiv.org/abs/2208.08544

Sept. 23, 2022, 1:12 a.m. | Dennis Frauen, Stefan Feuerriegel

cs.LG updates on arXiv.org arxiv.org

Estimating individual treatment effects (ITEs) from observational data is
relevant in many fields such as personalized medicine. However, in practice,
the treatment assignment is usually confounded by unobserved variables and thus
introduces bias. A remedy to remove the bias is the use of instrumental
variables (IVs). Such settings are widespread in medicine (e.g., trials where
compliance is used as binary IV). In this paper, we propose a novel, multiply
robust machine learning framework, called MRIV, for estimating ITEs using
binary …

arxiv binary effects treatment

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