• Dr. D. Mabuni

Causal relationships are popular and common in modern data analytics with machine learning techniques. Graphical causal data models are one special attractive and dominant use in many day to day applications. Probabilistic graphical causal data models are also becoming common and their usage is inevitable in numerous artificial intelligence and machine learning applications. Causal decision trees are scalable, fast, automatic and budding data analytics models. Probabilistic causal decision trees are more attractive, convenient and very probable causal data models in medical diagnosis, research, business etc. In this paper two probabilistic causal decision trees are constructed. The first is constructed by taking one hypothetical dataset for easy understanding purpose and the second one is constructed by taking another Adult UCI machine learning dataset. Probabilistic causal decision tree created in this paper for adult dataset is exactly same with causal decision tree created earlier for adult dataset with other standard frameworks. These results show that the proposed probabilistic causal decision tree creation framework is correct, efficient and very useful for taking effective decisions in many real-life applications.

probabilistic causal decision trees, causal decision trees, graphical causal models, probability.

"Probabilistic Causal Decision Trees", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.a207-a214, July-2023, Available :http://www.jetir.org/papers/JETIR2307022.pdf

"Probabilistic Causal Decision Trees", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppa207-a214, July-2023, Available at : http://www.jetir.org/papers/JETIR2307022.pdf