Understanding the Assumptions Underlying Instrumental Variable Analyses: a Brief Review of Falsification Strategies and Related Tools

• Core Purpose: This review advocates for the systematic use of falsification strategies and statistical tools to assess the plausibility of the unverifiable assumptions underlying Instrumental Variable (IV) analyses, especially in Mendelian Randomization (MR).
• Methods: While the Relevance assumption is statistically testable (e.g., F-statistic), the Exclusion Restriction and Independence assumptions can be tested for violation using methods like Negative Control Outcomes and sensitivity analyses (e.g., MR-Egger regression).
• Recommendation: The paper stresses that epidemiologists should move beyond justifying IV assumptions solely with subject matter knowledge and routinely employ formal statistical checks to improve the robustness and transparency of causal inference.

PubMed: 30148040 DOI: 10.1007/s40471-018-0152-1 Overview generated by: Gemini 2.5 Flash, 28/11/2025

Purpose and Scope of Review

Instrumental Variable (IV) methods, including their application in genetic epidemiology (Mendelian Randomization, MR), rely on three core assumptions to yield valid causal estimates. This review addresses the limitation that discussions about these assumptions often rely too heavily on subject matter knowledge. It systematically outlines and advocates for the use of various falsification strategies and related statistical tools to complement subject matter knowledge, test for assumption violations, and quantify potential bias.

Assumptions of Instrumental Variable Analysis

The validity of IV analysis rests on three critical conditions for the instrument (\(Z\)), the exposure (\(X\)), and the outcome (\(Y\)):

1. Relevance: The instrument (\(Z\)) must be associated with the exposure (\(X\)). (Statistically verifiable).
2. Exclusion Restriction: The instrument (\(Z\)) must only affect the outcome (\(Y\)) through the exposure (\(X\)). (Unverifiable).
3. Independence: The instrument (\(Z\)) must not be associated with any unmeasured confounders of the exposure-outcome relationship. (Unverifiable).

Falsification Strategies and Statistical Tools

The core of the review focuses on synthesizing and promoting the use of tools that assess the plausibility of the unverifiable assumptions (Exclusion Restriction and Independence).

Assessing Relevance

The relevance assumption can be tested statistically, most commonly using the F-statistic. A low F-statistic (typically \(<10\)) signals a weak instrument, which leads to bias toward the confounded observational association, and therefore violates the requirement for robust IV inference.

Assessing Exclusion Restriction and Independence

While these assumptions cannot be proven true, they can often be refuted or their potential bias quantified: * Negative Control Outcomes: This involves testing the IV estimate on an outcome that is theoretically known not to be affected by the exposure. A statistically significant IV estimate on a negative control outcome strongly suggests a violation of the exclusion restriction (e.g., horizontal pleiotropy in MR). * Sensitivity Analyses: Methods designed to detect violations of the assumptions, such as: * Egger Regression Intercept: Used in two-sample MR, the intercept can test for directional pleiotropy, a major violation of the exclusion restriction. * Multiple Instrument Methods: Comparing results from different sets of instruments or weighted analyses can help identify and mitigate potential bias stemming from a single instrument violating an assumption. * Known-Unknown Comparisons: Comparing the IV-derived causal estimate for an outcome where the true causal effect is already known (e.g., from a randomized trial) can validate the method in a specific context. * Bias Estimation Tools: Various techniques exist to estimate the magnitude or direction of bias that would be expected if the assumptions were violated by a certain amount.

Conclusions and Recommendations

The authors conclude that although the core IV assumptions remain unverifiable, the widespread availability of statistical tools and falsification strategies means that researchers should no longer rely solely on subject matter knowledge to justify their IV results. They strongly recommend that epidemiologists systematically apply these formal statistical checks and sensitivity analyses to rigorously test for potential assumption violations and thereby increase the credibility and transparency of causal effects estimated using IV methods.