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Efficient covariate balancing for the average treatment effect with missing outcome

Author

Listed:
  • Tang, Shengfang
  • Zhan, Mingfeng
  • Jiang, Qingshan
  • Zhang, Tong

Abstract

This paper develops an empirical balancing approach for the estimation of treatment effects under the framework of outcomes being suffered from missing. We first represent the parameter of interest as a weighted expectation of the observed outcome by introducing two auxiliary binary variables and then estimate the weighting functions using covariate balancing methods. By tailoring the loss function for the weighting functions, the resulting treatment effect estimates are automatically weight-normalized and exhibit both low bias and reduced variance in finite samples when compared to conventional inverse probability weighting methods. Under some regularity conditions, we show that the proposed estimator is consistent, asymptotically normally distributed with the asymptotic variance achieving the semiparametric efficiency bound. Finite-sample performance of the proposed method is evaluated via Monte Carlo simulations.

Suggested Citation

  • Tang, Shengfang & Zhan, Mingfeng & Jiang, Qingshan & Zhang, Tong, 2024. "Efficient covariate balancing for the average treatment effect with missing outcome," Economics Letters, Elsevier, vol. 244(C).
    • Handle: RePEc:eee:ecolet:v:244:y:2024:i:c:s0165176524004452
    DOI: 10.1016/j.econlet.2024.111961
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