HIGHLIGHTS
- What: The article proposes a methodological framework for researchers to customize the utility of a decision maker in evaluating which models are preferred in generating counterfactual predictions. The works discussed at the workshop are grouped into the following three core themes focused on leveraging recent advances in ML to improve estimation in the context of generalizability: Estimating Heterogeneous Effects. The authors provide a few examples of key discussion points from the workshop. (1) Addressing Data Harmonization Challenges: Most methods in the literature, including nearly all the work , require data that is harmonized across trials and populations.
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