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About this paper
Alejandro Schuler, Michael Baiocchi, Robert Tibshirani, Nigam Shah
This paper systematically compares different approaches for model selection when estimating individual treatment effects (ITEs) through a comprehensive simulation study. Researchers from Stanford University found that the R-learner objective function ($\\hat{\\tau}\\\text{-risk}\\\ ext{R}$) consistently selected ITE models with the lowest estimation error and highest policy value, providing a reliable method for choosing among competing ITE models.
Key insights
- The R-learner objective function ($\\hat{\\tau}\\\text{-risk}\\\ ext{R}$) proved to be the most consistently effective metric for selecting ITE models, excelling across various simulation scenarios.
- Counter-intuitively, simple prediction error ($\\hat{\\mu}\\\text{-risk}$) and its IPTW-weighted version ($\\hat{\\mu}\\\text{-risk}\\\ ext{IPTW}$) performed surprisingly well for ITE model selection, sometimes surpassing metrics explicitly designed for policy value maximization.
- Metrics directly optimizing policy value ($\\hat{v}\\\text{IPTW}$, $\\hat{v}\\\text{DR}$) were generally suboptimal for model selection, despite being effective for evaluating the policy of a fixed model.
Summary by alphaXiv.
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