Predictive models can mislead treatment effects—BAC shows how to fix hidden confounding
Standard variable‑selection tricks—stepwise, Lasso, BIC—pick predictors that explain the outcome, not the hidden causes of treatment. That leaves out confounders that strongly affect the exposure, biasing the estimated effect. Bayesian Adjustment for Confounding (BAC) jointly models exposure and outcome, restoring valid causal estimates.