Individuals have heterogeneous outcomes from interventions. In a clinical setting, estimating how patients will respond to different treatments is critical for targeted care. Clinicians constantly ask themselves, given a patient’s history, what would happen to the patient’s clinical trajectory if they were given one treatment versus another. However, in practice it is often unknown how the patient’s signals will change in response to treatment until that treatment is actually administered. Even then, it is impossible to observe the counterfactual from real data, i.e., what would have happened to the patient if the doctor had made a different choice. In order to solve this causal question, we use the g-formula with proper assumptions to estimate physiologic trajectories and treatment responses from observed data. To demonstrate this we model blood pressure and heart rate for patients in the intensive care unit (ICU) and estimate their responses to six types of treatments that are used in their management. These two signals are among the most commonly used vital signs in the ICU and are critical for identifying life-threatening conditions like septic and hemorrhagic shock. To model the signal with treatment response from observed data, we use two different Bayesian non-parametric (BNP) methods to build the estimator. BNP are known to have an extremely flexible functional form, which helps to overcome the model mis-specification problem and makes the estimator more robust.