Causal inference is a familiar topic in biomedical research and a key concept in the study of connectivity in various physiological systems. This work aimed to analyse the coupling between the beat to beat parameters derived from ECG and respiration. It was the first time such an analysis was carried out in the context of finding the differences caused by chemical's exposure. We used conditional Granger causality, a popular method to evaluate direct causal relationships. We have incorporated the cardinality constraint in the optimization function of Granger causality (GC) to deal with the high dimensionality challenge. Further, we extended the original formulation GC to evaluate the coupling between two unequally sampled signals. Finally, end to end implementation of the machine learning prediction model using causal features is well illustrated. We found a consistent decrease in the average coupling strength of breathing parameters after the exposure. But in the case of ECG interactions, no noticeable change was observed. Surprisingly we found no significant links between the ECG and breathing parameters. The support vector machine (SVM) and random forest trained on coupling values differentiate between healthy and exposure samples. The accuracy of trained SVM and random forest on the independent test set were 78 % and 75 %, respectively.