Incorporating the propensity of an antibiotic engendering resistance to itself and other antibiotics is a potentially useful strategy for preventing antimicrobial resistance (AMR). However, prospective studies have been difficult to generalize to outpatients and retrospective studies are prone to design errors and model misspecification. To address this gap, we apply causal inference with Targeted Maximum Likelihood Estimation using machine learning, to data from the Electronic Health Record to define the antibiotic use (Treatment) - resistance (Outcome) relationship for common outpatient therapies used to treat urinary tract infection (UTI). We used total 20 covariates (Confounder) to adjust for the confounding effect of treatment on outcome. By estimating the treatment effect of antibiotics on future resistance, we expect to derive clinical correlation which helps build a decision support tool. Estimating the effect of antibiotic treatment will help clinicians design better care plans for patients by choosing the best antibiotics that minimize the risk of future AMR event.