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The oracle property of the generalized outcome-adaptive lasso

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  • Baldé, Ismaila

Abstract

The generalized outcome-adaptive lasso (GOAL) is a variable selection for high-dimensional causal inference proposed by Baldé et al. (2023). When the dimension is high, it is now well established that an ideal variable selection method should have the oracle property to ensure the optimal large sample performance. However, the oracle property of GOAL has not been proven. In this paper, we show that the GOAL estimator enjoys the oracle property. Our simulation shows that the GOAL method deals with the collinearity problem better than the oracle-like method, the outcome-adaptive lasso (OAL).

Suggested Citation

  • Baldé, Ismaila, 2025. "The oracle property of the generalized outcome-adaptive lasso," Statistics & Probability Letters, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:stapro:v:221:y:2025:i:c:s0167715225000240
    DOI: 10.1016/j.spl.2025.110379
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    References listed on IDEAS

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    1. Susan M. Shortreed & Ashkan Ertefaie, 2017. "Outcome‐adaptive lasso: Variable selection for causal inference," Biometrics, The International Biometric Society, vol. 73(4), pages 1111-1122, December.
    2. Yanming Li & Hyokyoung G. Hong & S. Ejaz Ahmed & Yi Li, 2019. "Weak signals in high‐dimensional regression: Detection, estimation and prediction," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 35(2), pages 283-298, March.
    3. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    4. Khalili, Abbas & Chen, Jiahua, 2007. "Variable Selection in Finite Mixture of Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1025-1038, September.
    5. Ismaila Baldé & Yi Archer Yang & Geneviève Lefebvre, 2023. "Reader reaction to “Outcome‐adaptive lasso: Variable selection for causal inference” by Shortreed and Ertefaie (2017)," Biometrics, The International Biometric Society, vol. 79(1), pages 514-520, March.
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