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[2306.14297] Inference for relative sparsity
Summary
The paper discusses a novel approach to inference for relative sparsity in healthcare decision-making, addressing the need for uncertainty characterization in policy estimation.
Why It Matters
This research is significant as it enhances the understanding of how to derive healthcare policies that deviate from standard care while ensuring that these deviations are justifiable and statistically sound. The focus on inference, rather than just estimation, is crucial for practical applications in medical settings.
Key Takeaways
- Introduces a relative sparsity penalty for healthcare policy estimation.
- Addresses the challenges of inference in multi-stage decision-making scenarios.
- Proposes a sample-splitting framework for post-selection inference.
- Implements an adaptive relative sparsity penalty to improve stability.
- Utilizes real electronic health record data for validation.
Statistics > Methodology arXiv:2306.14297 (stat) [Submitted on 25 Jun 2023 (v1), last revised 15 Feb 2026 (this version, v2)] Title:Inference for relative sparsity Authors:Samuel J. Weisenthal, Sally W. Thurston, Ashkan Ertefaie View a PDF of the paper titled Inference for relative sparsity, by Samuel J. Weisenthal and 2 other authors View PDF HTML (experimental) Abstract:In healthcare, there is much interest in estimating policies, or mappings from covariates to treatment decisions. Recently, there is also interest in constraining these estimated policies to the standard of care, which generated the observed data. A relative sparsity penalty was proposed to derive policies that have sparse, explainable differences from the standard of care, facilitating justification of the new policy. However, the developers of this penalty only considered estimation, not inference. Here, we develop inference for the relative sparsity objective function, because characterizing uncertainty is crucial to applications in medicine. Further, in the relative sparsity work, the authors only considered the single-stage decision case; here, we consider the more general, multi-stage case. Inference is difficult, because the relative sparsity objective depends on the unpenalized value function, which is unstable and has infinite estimands in the binary action case. Further, one must deal with a non-differentiable penalty. To tackle these issues, we nest a weighted Trust Region Policy Optimization f...
