MH
M. Havelka
Authored
2 records found
Causal machine learning is a relatively new field which tries to find a causal relation between the treatment and the outcome, rather than a correlation between the features and the outcome. To achieve this, many different models were proposed, one of which is the causal forest.
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The field of causal inference provides a variety of estimators that can be used to find the effect of a treatment on an outcome based on observational data. However, many of these estimators require the unconfoundedness assumption, stating that all relevant confounders are observ
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