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Found 2 papers, 1 papers with code
A framework for causal segmentation analysis with machine learning in large-scale digital experiments
We present an end-to-end methodological framework for causal segment discovery that aims to uncover differential impacts of treatments across subgroups of users in large-scale digital experiments.
A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure
Incorporating working assumptions during estimation is more robust than assuming they hold in the underlying causal model.
Methodology
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