The query of causality is of paramount importance in biomedical data analysis: assessing the causal relationships between the observed variables allows to improve our understanding of the tackled medical condition and better support decision-making. Torsade de Pointes (TdP) is an extremely serious drug-induced cardiac side effect, which can provoke ventricular fibrillation and lead to sudden death. TdP is related to abnormal repolarizations in single cells, and the minimum set of ion channels needed to correctly assess TdP risk is still an open question. Discovering causal relations between drug-induced ionic channels' perturbations could shed new light on the underlined mechanisms leading to TdP, and drive variable selection to improve TdP-risk assessment. In this work, we propose to apply the causal discovery method ICA-Linear Non-Gaussian Acyclic Model (ICA-LiNGAM) to uncover the relations across the 7 ion channels identified by the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative as potentially related to the induction of TdP: IKr, INa, INaL, ICaL, IK1, IKs, and Ito. The obtained Bayesian causal network can be explored to infer the downstream impact of the ionic currents in the drug's safety label. We consider 109 drugs of known torsadogenic risk (51 unsafe) listed by CredibleMeds. We identify IKr, INaL, and ICaL as the ones which directly affect TdP-risk assessment, and suggest that \Ina\ perturbations could potentially have a high impact on proarrhythmic risk induction. Our causality-based results were further confirmed by independently performing binary drug risk classification, which shows that the combination of the 3 selected ions maximizes the classification accuracy and specificity, outperforming state-of-the-art approaches based on alternative ion channel combinations.