🤖 AI Summary
This study addresses the bias in causal effect estimation arising from the coexistence of unmeasured cluster-level confounding and treatment effect heterogeneity in observational clustered data. To tackle this dual challenge, the authors propose an intra-group g-computation approach: clusters are first stratified by observed treatment prevalence, within-stratum g-computation is implemented using random-effects models, and estimates across strata are then aggregated to correct for bias. This method innovatively embeds random-effects modeling within the g-computation framework, effectively mitigating both sources of bias. Simulation studies demonstrate that the proposed estimator achieves the lowest root mean squared error when unmeasured confounding and heterogeneity co-occur. Applied to data from Bangladesh, the method reveals that adolescent pregnancy is associated with an average reduction of 0.12 in child height-for-age Z-scores (95% CI: [–0.18, –0.06]).
📝 Abstract
Observational studies frequently involve hierarchical data structures in which individuals are nested within higher-level units. In such settings, unmeasured cluster-level factors may confound the treatment-outcome relationship and may additionally induce treatment effect heterogeneity across clusters, complicating causal effect estimation. We formalize the use of g-computation for hierarchical observational data by incorporating random-effects models (REM) as outcome models and propose a within-group g-computation strategy designed to reduce bias arising from unmeasured cluster context. The approach groups clusters according to their observed treatment prevalence and performs g-computation within groups before aggregating group-specific estimates. Through extensive Monte Carlo simulations, we compare the standard and within-group implementations of g-computation using both linear models and REM. Results show that both standard and within-group REM-based implementations substantially reduce bias when the unmeasured cluster-level variable acts solely as a confounder, whereas the proposed within-group REM estimator achieves the lowest RMSE when the unmeasured cluster-level factor acts as both a confounder and a source of treatment effect heterogeneity. We apply the proposed within-group REM estimator to estimate the causal effect of adolescent pregnancy on the child height-for-age Z-score using 2019 Bangladesh MICS data, obtaining an estimated effect of -0.12 (95% bootstrap CI: [-0.18, -0.06]). The proposed within-group g-computation framework offers a strategy for reducing bias from unmeasured cluster-level confounding and treatment effect heterogeneity in hierarchical observational studies.