Cardiotoxicity is a relatively common and particularly important adverse event caused by chemotherapy for breast cancer patients. Typical associative phenotypes, such as risk factors associated with diabetes, can often be detected solely based on the data elements existing in electronic health records; however, causal phenotypes, such as risk factors causing cardiotoxicity, require establishing causation between chemotherapy and determining new heart disease, and cannot be directly observedfrom EHR. We propose three phenotyping algorithms to assess breast cancer patients' susceptibility to cardiotoxicity caused by five first-line antineoplastic drugs: (1) causal phenotype model to predict the patients' risk of cardiotoxicity as the difference between the heart disease risks with exposure and nonexposure to the drugs; (2) regular predictive model; (3) combined predictive model of the above two models. Concordances for three methods were 0.60, 0.62, and 0.68. When considering all exposed patients, concordances were 0.66, 0.58 and 0.65 at 280 days after treatment. The study demonstrates the potential utility of causal phenotyping.