• Corpus ID: 251224027

Bias Formulas for Violations of Proximal Identification Assumptions

@inproceedings{Cobzaru2022BiasFF,
  title={Bias Formulas for Violations of Proximal Identification Assumptions},
  author={Raluca Cobzaru and Roy E. Welsch and Stan N. Finkelstein and Kenney Ng and Zach Shahn},
  year={2022}
}
Causal inference from observational data often rests on the unverifiable assumption of no unmeasured confounding. Recently, Tchetgen Tchetgen and colleagues have introduced proximal inference to leverage negative control outcomes and exposures as proxies to adjust for bias from unmeasured confounding [16]. However, some of the key assumptions that proximal inference relies on are themselves empirically untestable. Additionally, the impact of violations of proximal inference assumptions on the… 
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References

SHOWING 1-10 OF 13 REFERENCES

A More Credible Approach to Parallel Trends ∗

This paper proposes tools for robust inference in difference-in-differences and event-study designs where the parallel trends assumption may be violated. Instead of re-quiring that parallel trends…

Multiply robust causal inference with double‐negative control adjustment for categorical unmeasured confounding

This work establishes non-parametric identification of the ATE under weaker conditions in the case of categorical unmeasured confounding and negative control variables, and provides a general semiparametric framework for obtaining inferences about the AtE while leveraging information about a possibly large number of measured covariates.

Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome

This paper proposes a simple technique for assessing the range of plausible causal con- clusions from observational studies with a binary outcome and an observed categorical covariate. The technique…

Sensitivity analysis for certain permutation inferences in matched observational studies

SUMMARY In observational studies, treatments are not randomly assigned to experimental units, so that randomization tests and their associated interval estimates are not generally applicable. In an…

Sensitivity Analysis for Selection bias and unmeasured Confounding in missing Data and Causal inference models

In both observational and randomized studies, subjects commonly drop out of the study (i.e., become censored) before end of follow-up. If, conditional on the history of the observed data up to t, the…

Principles of confounder selection

This paper puts forward a practical approach to confounder selection decisions when the somewhat less stringent assumption is made that knowledge is available for each covariate whether it is a cause of the exposure, and whetherIt is aCause of the outcome.

Estimating causal effects of treatments in randomized and nonrandomized studies.

A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented. The objective is to specify the benefits of randomization in estimating…

Identifying Causal Effects With Proxy Variables of an Unmeasured Confounder.

This work shows that, with at least two independent proxy variables satisfying a certain rank condition, the causal effect is nonparametrically identified, even if the measurement error mechanism, i.e., the conditional distribution of the proxies given the confounder, may not be identified.

Smoking and lung cancer: recent evidence and a discussion of some questions.

The consistency of all the epidemiologic and experimental evidence supports the conclusion of a causal relationship with cigarette smoking, while there are serious incon-sistencies in reconciling the evidence with other hypotheses which have been advanced.

Good practices for quantitative bias analysis.

A guide to good practices for conducting and presenting bias analyses is described to encourage more widespread use of bias analysis to estimate the potential magnitude and direction of biases, as well as the uncertainty in estimates potentially influenced by the biases.