Work

Selection without Collider Stratification can Bias Instrumental Variable Analyses in the Presence of Causal Effect Heterogeneity

Year: 2026

Type: preprint

Abstract: Selection bias is a common concern in causal inference. Two mechanisms can give rise to selection bias: collider bias or stratifying on an effect modifier of the exposure-outcome causal effect. Research into the impact of selection bias on instrumental variable (IV) analyses has focused primarily on collider bias. Here, we show that IV estimates can be biased if selection is an effect modifier (or caused by an effect modifier) of the exposure-outcome effect. In a simulation study, we investigate the size and direction of the bias when estimating effects on the mean difference or odds ratio scale. Bias can also occur due to heterogeneity in the instrument-exposure relationship, although its magnitude is typically smaller. Finally, we discuss methods to deal with this bias in practice, including inverse probability weighting (IPW).

Source: medRxiv

DOI: 10.64898/2026.09.27.26364122

Authors: Apostolos Gkatzionis, Fernando Pires Hartwig, Kate Tilling

Institutions: University of Bristol, MRC Integrative Epidemiology Unit, Universidade Federal de Pelotas

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: University of Bristol, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Medical Research Council

Awards: G1450271589, G2822846613, G8383967617