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Found 2 papers, 2 papers with code
Proximal Causal Inference With Text Data
We propose a new causal inference method that uses multiple instances of pre-treatment text data, infers two proxies from two zero-shot models on the separate instances, and applies these proxies in the proximal g-formula.
On the consistency of supervised learning with missing values
A striking result is that the widely-used method of imputing with a constant, such as the mean prior to learning is consistent when missing values are not informative.
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