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Related Experiment Videos

Instrumental variables as bias amplifiers with general outcome and confounding.

P Ding1, T J VanderWeele2, J M Robins2

  • 1Department of Statistics, University of California, Berkeley, California, USA.

Biometrika
|October 17, 2017
PubMed
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Adjusting for more covariates in observational studies can increase bias when unmeasured confounding exists. This study provides a general theory for this "bias amplification" phenomenon beyond linear models.

Area of Science:

  • Causal inference
  • Observational studies
  • Statistical modeling

Background:

  • Causal inference from observational data relies on unconfoundedness, often assuming more covariates improve estimation.
  • However, adjusting for certain pretreatment covariates can amplify bias in the presence of unmeasured confounding.

Purpose of the Study:

  • To generalize the understanding of bias amplification beyond linear models.
  • To provide a theoretical framework for when adjusting for covariates increases causal effect estimation bias.

Main Methods:

  • Developed a general theory for bias amplification under monotonicity assumptions.
  • Analyzed additive and multiplicative treatment models conditional on instrumental variables and confounders.

Main Results:

Keywords:
Causal inferenceDirected acyclic graphInteractionMonotonicityPotential outcome

Related Experiment Videos

  • Demonstrated that bias amplification occurs in a wide class of non-linear models.
  • Showed that monotonicity assumptions in specific models correspond to causal diagram structures.

Conclusions:

  • The common practice of adjusting for all pretreatment covariates may be detrimental when unmeasured confounding is present.
  • This work extends the theory of bias amplification, offering crucial insights for robust causal inference in observational studies.