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@article{205104,
        author = {Dr. D. Mabuni},
        title = {Smallest Causal Decision Tree Creation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {5205-5212},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205104},
        abstract = {Causal inference concepts are inherently present in many real-world tasks. Cause-and-effect relationships are frequently occurring in many applications. In any system if causal inference knowledge is known then it will be easy to take effective decisions under risky conditions. Decision trees such as C4.5 and CART are popular classification algorithms. Causal decision trees can be created by combining with the best features of normal decision tree and popular statistical parameters. Splitting procedure of normal decision tree is different from the splitting procedure of causal decision tree. In causal decision tree splitting is done based on values of treatment effects. Causal decision trees can be created using different ideas, plans and procedures. Correlations between predictor attributes and target attribute play an important role. One way of creating causal decision tree is by using all the attributes in the training dataset. Alternate way for creating causal decision tree is use only highly correlated attributes and remove attributes whose correlation value is less than the specified correlation threshold. In this paper both above said causal decision trees are created. Correlation threshold is also variable according to desired requirements.},
        keywords = {machine learning, decision tree, causal decision tree, treatment effects, causal inference, causal relationships, data analytics, decision tree analysis, causal Bayesian networks (CBNs), correlation, smallest causal decision tree. Curse of dimensional, statistical correlation, adult dataset, UCI machine learning.},
        month = {June},
        }