Observational studies reporting adjusted associations between childhood body mass index (BMI) rebound and subsequent cardio-metabolic outcomes have often not given explicit attention to causal inference, including definition of a target causal effect and assumptions for unbiased estimation of that effect. Using data from 649 children in a Boston, Massachusetts-area cohort recruited in 1999-2002, we considered effects of stochastic interventions on a chosen subset of modifiable, yet unmeasured, exposures expected to be associated with early (< age 4 years) BMI rebound (a proxy) on adolescent cardiometabolic outcomes. We consider assumptions under which these effects may be identified with available data. This leads to an analysis where the proxy, rather than exposure, acts as exposure in the algorithm. We applied Targeted Maximum Likelihood Estimation, a doubly-robust approach that naturally incorporates machine learning for nuisance parameters (e.g. propensity score). We estimated a protective effect of an intervention that assigns modifiable exposures according to the distribution in the observational study of those without (vs. with) early BMI rebound for fat-mass index (-1.39 kg/m2; 95% CI -1.63,-0.72), but weaker or no effects for other cardiometabolic outcomes. Our results clarify distinctions between algorithms and causal questions, encouraging explicit thinking in causal inference with complex exposures.