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Causal inference on quantiles with an obstetric application.

Zhiwei Zhang1, Zhen Chen, James F Troendle

  • 1Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland 20993, USA. zhiwei.zhang@fda.hhs.gov

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Summary

This study introduces new statistical methods for estimating quantiles of potential outcomes in observational studies, like the Consortium on Safe Labor (CSL). These methods improve causal inference beyond just population means, offering more detailed insights.

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Area of Science:

  • Statistics
  • Causal Inference
  • Observational Studies

Background:

  • Current causal inference focuses on population means, neglecting other vital statistics like quantiles.
  • Observational studies require robust methods for estimating treatment effects on various outcome measures.

Purpose of the Study:

  • To develop and compare methods for estimating marginal quantiles of potential outcomes.
  • To estimate quantiles among treated individuals in observational data.
  • To address limitations in current statistical practice for causal inference.

Main Methods:

  • Adaptation of existing statistical techniques for quantile estimation.
  • Development of estimators using outcome regression (OR), inverse probability weighting, and stratification.
  • Creation of a doubly robust (DR) estimator and a hybrid estimator incorporating stratification for enhanced stability.

Main Results:

  • Proposed methods provide reliable estimation of quantiles of potential outcomes.
  • The hybrid estimator offers improved numerical stability, though with a potential for slight bias under specific model misspecifications.
  • Methods demonstrated effectiveness on Consortium on Safe Labor (CSL) data and simulation studies.

Conclusions:

  • The developed methods extend causal inference to quantiles, offering richer insights than mean-based approaches.
  • The hybrid estimator presents a valuable tool for handling complex observational data, balancing stability and accuracy.
  • Findings are applicable to obstetric labor progression studies and other observational research settings.