Two robust tools for inference about causal effects with invalid instruments.

Area of Science:

• Epidemiology
• Biostatistics
• Econometrics

Background:

• Instrumental variables (IV) are crucial for estimating causal effects when unmeasured confounding is present.
• Traditional IV methods assume all instruments are valid, a strong assumption often violated in practice.
• Invalid instruments can lead to biased causal effect estimates and unreliable inference.

Purpose of the Study:

• To develop novel statistical tools for valid causal inference in the presence of invalid instrumental variables.
• To provide methods for constructing reliable confidence intervals and performing hypothesis tests under relaxed IV assumptions.
• To offer a sensitivity analysis framework for situations where IV validity is uncertain.

Main Methods:

• Proposes a general approach for constructing confidence intervals by taking unions of existing intervals.
• Introduces a new test for the null causal effect utilizing collider bias.
• Applies the developed methods to a Mendelian randomization study.

Main Results:

• The proposed methods demonstrate superior performance compared to traditional IV confidence intervals when invalid instruments are present.
• The novel test effectively detects null causal effects under specific conditions of instrument invalidity.
• The approach provides a valuable tool for sensitivity analyses in IV studies.

Conclusions:

• The developed tools enable valid statistical inference and hypothesis testing even with invalid instrumental variables.
• These methods enhance the reliability of causal effect estimation in observational studies and Mendelian randomization.
• The approach serves as a crucial sensitivity analysis for assessing the robustness of IV findings.