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Found 2 papers, 1 papers with code
Causal and counterfactual views of missing data models
It is often said that the fundamental problem of causal inference is a missing data problem -- the comparison of responses to two hypothetical treatment assignments is made difficult because for every experimental unit only one potential response is observed.
Double/Debiased Machine Learning for Treatment and Causal Parameters
Fortunately, this regularization bias can be removed by solving auxiliary prediction problems via ML tools.
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