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Strategies for Assessing and Addressing Confounding

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Confounding can be addressed at both the design phase of a study and through analytical methods after data...
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Related Experiment Video

Updated: Jul 14, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Bounds on potential risks and causal risk differences under assumptions about confounding parameters.

Yasutaka Chiba1, Tosiya Sato, Sander Greenland

  • 1Department of Biostatistics, Kyoto University School of Public Health, Kyoto, Japan. chibay@pbh.med.kyoto-u.ac.jp

Statistics in Medicine
|May 26, 2007
PubMed
Summary

This study refines nonparametric bounds for causal effects in observational studies by incorporating confounding bias assumptions. These refined bounds offer tighter estimations for treatment effects, even with imperfect compliance in randomized trials.

Related Experiment Videos

Last Updated: Jul 14, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Causal inference
  • Observational studies
  • Biostatistics

Background:

  • Nonparametric bounds for causal effects are established under deterministic potential-outcome models.
  • Confounding bias, defined as differences in expected potential outcomes between exposed and unexposed groups, impacts effect estimation.
  • Existing methods provide bounds but can be widened with additional assumptions.

Purpose of the Study:

  • To derive narrower nonparametric bounds on causal effects in observational studies.
  • To investigate the impact of confounding bias assumptions on these bounds.
  • To extend these bounds to randomized studies with noncompliance.

Main Methods:

  • Developing narrower nonparametric bounds by incorporating assumptions about confounding bias.
  • Defining confounding bias as the expectation difference of potential outcomes between exposed and unexposed groups.
  • Deriving bounds for randomized studies with noncompliance, relating them to the per-protocol effect.

Main Results:

  • Crude effect measures can bound causal effects under the specified assumptions.
  • Bounds for randomized studies with noncompliance are derived and linked to the per-protocol effect.
  • Identifiable direction of effect is achieved under assumptions with perfect compliance in one group.

Conclusions:

  • The derived assumptions, while not directly identifiable, are reasonable in certain contexts.
  • The refined bounds offer improved precision for causal effect estimation in observational and noncompliant randomized studies.
  • This work advances methods for causal effect quantification under realistic study conditions.