• S.SAJIDA
• Dr. K.Vijaya Lakshmi
• Prof.M.Padmavathamma

In computer science causality is a fundamental notion and it plays a vital role in exploration and prediction of Decision making control. Many real time application scenarios, it is very useful to discover causal relationships between multiple causal variables and single predicted outcome variable. Causal decision trees (CDT) construction is similar to the classification decision tree construction but the former uses different static based framework for finding causal relationships between variables. Sometimes two or more causal decision trees will be created for the given same dataset with pre specified tree height. Present study proposes new techniques to find the best causal probability decision tree from among the many possible causal decision trees. Extensive experiments are conducted and the results show that proposed techniques are reasonably good in finding optimal causal probability decision trees.

CDT,causal Decision trees, node size, node probability, node type, node causality, aggregated score

"IMPLEMENTAION OF OPTIMAL CAUSAL PROBABILITY DECISION TREES IN DIFFERENT SCENARIOS TO DISCOVER CAUSAL RELATIONSHIPS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 9, page no.d8-d12, September-2022, Available :http://www.jetir.org/papers/JETIR2209303.pdf

"IMPLEMENTAION OF OPTIMAL CAUSAL PROBABILITY DECISION TREES IN DIFFERENT SCENARIOS TO DISCOVER CAUSAL RELATIONSHIPS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 9, page no. ppd8-d12, September-2022, Available at : http://www.jetir.org/papers/JETIR2209303.pdf