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[2302.05260] Policy Learning with Rare Outcomes
Source: arxiv.org
Article summary:
1. This article compares the performance of various policy learning algorithms using synthetic data with varying outcome prevalence, positivity violations, extent of treatment effect heterogeneity and sample size.
2. The plug-in policy type outperforms tree-based policies regardless of ML method used.
3. The methods are applied to a case study that investigates infant mortality through improved targeting of subsidised health insurance in Indonesia.
Article analysis:
The article is generally reliable and trustworthy as it provides evidence for its claims from a case study conducted in Indonesia, which adds credibility to the findings presented in the article. Additionally, the authors provide detailed information about the synthetic data used to compare the performance of various policy learning algorithms, which further strengthens their conclusions. However, there are some potential biases that should be noted when considering this article. For example, the authors do not explore any counterarguments or present both sides equally when discussing their findings; instead they focus solely on presenting their own conclusions without considering any other perspectives or opinions on the matter. Additionally, there is no mention of possible risks associated with implementing these policies or any discussion of how they could potentially be misused or abused by those in power. Finally, there is a lack of detail regarding how exactly these policies would be implemented in practice and what safeguards would be put in place to ensure that they are used responsibly and ethically.