Fig 1. Causal directed acyclic graph (DAG) for estimating the effect of smoking cessation (A) on co-occurring conditions (Y) under various assumptions about the causal structure of the data.

1A depicts assumptions for the adjusted models with following temporal ordering of variables: L₁, A₁, M₁. Some potential time-varying covariates (L₁) measured at the first follow-up visit and V₀ are included for the weights. 1B depicts assumptions for the adjusted models with following temporal ordering of variables: A₁, L₁, M₁. All potential time-varying covariates measures at the first follow-up visit are excluded from the weight model. Only V₀ included for the weights. 1C depicts assumptions for the adjusted models with following temporal ordering of variables: L₁, M₁, A₁. All potential time-varying covariates measures at the first follow-up visit and V₀ are included for the weights. V₀ Baseline covariates (e.g., HIV status, race, age, education, income), past year unhealthy alcohol use, cannabis, cocaine, other stimulants at time 0, and current smoking, depression, anxiety, and pain measured at time 0. L₁ Past year unhealthy alcohol use, cannabis, cocaine, other stimulants and opioids measured at first follow-up visit. A₁ Current cigarette smoking measured at first follow-up visit. M₁ Current depression, anxiety, and pain measured at first follow-up visit. Y₂ Outcome measured at second follow-up visit (for all outcomes).