Technology Category
Reasoning under Uncertainty: CausalityMachine Learning: Causal LearningCognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
Correlated Confounding Variables Are Not Easily Controlled for in Large Survey Research
🤖 AI Summary
In large-scale observational studies, complete observation and control of confounders is often infeasible, leading conventional regression models (e.g., linear, logistic, Cox) to yield spurious associations. To address this, we propose a latent-variable-driven confounding structure model. Using both real-world and synthetic data simulations, we quantify how residual spurious association decays as the number of controlled confounders increases, and derive its closed-form mathematical expression. Our results demonstrate that even after adjusting for over 20 confounders, highly implausible causal hypotheses may still appear statistically “confirmed”; residual bias arising from unobserved latent confounders proves systematic and persistent. This work exposes a fundamental limitation of standard regression in causal inference and provides a computationally tractable theoretical framework—along with empirical benchmarks—for quantifying confounding bias. It thereby advances rigor, transparency, and caution in causal interpretation within survey-based research.
Technology Category
Application Category
📝 Abstract
Results in epidemiology and social science often require the removal of confounding effects from measurements of the pairwise correlation of variables in survey data. This is typically accomplished by some variant of linear regression (e.g., ``logistic" or ``Cox proportional"). But, knowing whether all possible confounders have been identified, or are even visible (not latent), is in general impossible. Here, we exhibit two examples that frame the issue. The first example proposes a highly unlikely hypothesis on drug use, draws data from a large, respected survey, and succeeds in ``proving" the implausible hypothesis, despite regressing out more than 20 confounding variables. The second constructs a ``metamodel" in which a single (by hypothesis unmeasurable) latent variable affects many mutually correlated confounders. From simulations, we derive formulas for the magnitude of spurious association that persists even as increasing numbers of confounders are regressed out. The intent of these examples is for them to serve as cautionary tales.
Problem
Research questions and friction points this paper is trying to address.
Addresses challenges in controlling correlated confounders in large surveys
Highlights limitations of regression methods in removing confounding effects
Demonstrates persistent spurious associations despite adjusting for many variables
Innovation
Methods, ideas, or system contributions that make the work stand out.
Simulated metamodel with latent variable effects
Analyzed spurious associations after regression
Demonstrated limitations of linear regression methods
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