Estimation and Inference for Moments of Ratios with Robustness against Large Trimming Bias
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Abstract (arXiv)
Empirical researchers often trim observations with small denominator A when they estimate moments of the form E[B/A]. Large trimming is a common practice to mitigate variance, but it incurs large trimming bias. This paper provides a novel method of correcting large trimming bias. If a researcher is willing to assume that the joint distribution between A and B is smooth, then a large trimming bias may be estimated well. With the bias correction, we also develop a valid and robust inference result for E[B/A].
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Sep 2017.
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Citing papers per year: 2023: 2; 2024: 0; 2025: 1.
3 papers.
- 2023High Precision Causal Model Evaluation with Conditional Randomizationcited by 1
- 2023Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlapcited by 0
- 2025A Sensitivity Approach to Causal Inference Under Limited Overlapcited by 0
- 2025A Sensitivity Approach to Causal Inference Under Limited Overlap27 Nov 2025
- 2023High Precision Causal Model Evaluation with Conditional Randomization3 Nov 2023
- 2023Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap13 Jan 2023