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The Role of Confounding Variables in Causal Inference

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The Role of Confounding Variables in Causal Inference

In research, independent variables are the ones that are controlled or varied in order to establish their correlation to the dependent variable. The dependent variable is the variable that is being observed in order to determine the effects of the independent variables. On the other hand, there are extraneous variables that refer to any variable that is not of interest to the study but which has the capability of affecting the dependent variable.

There are two common strategies used to address extraneous variables: randomization and matching. Randomization helps in the allocation of the participants to groups in a random manner to eliminate bias. Matching: the subjects are matched in such a way that other extraneous variables are grouped together to avoid confounding. Another type is statistical control, in which the researcher employs statistical means for controlling for extraneous variables, such as through regression analyses (Byrnes and Dee, 2025).

For instance, in the paper by Egami and Tchetgen (2024), the authors employed negative control variables (auxiliary variables) to control unmeasured network confounding in causal peer effect estimation. By including outcome and exposure variables with no relation to the treatment of interest, they couldn’t be influenced by confounding and determine accurate causal peer effects with dependence in the network (Egami and Tchetgen, 2024).

Reference

Egami, N., & Tchetgen Tchetgen, E. J. (2024). Identification and estimation of causal peer effects using double negative controls for unmeasured network confounding. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 86(2), 487-511. https://doi.org/10.48550/arXiv.2109.01933

Byrnes, J. E., & Dee, L. E. (2025). Causal inference with observational data and unobserved confounding variables.  Ecology Letters,  28(1), e70023. https://onlinelibrary.wiley.com/doi/abs/10.1111/ele.70023