Causal Inference is the study of relationships between cause and effect. The main goal behind the process is to gather observations and determine what caused them, based on the data available. This is possible with the help of experiments and Hypothesis Testing. Causal Inference determines whether the predictive models and decisions really worked. For that reason, it’s one the building blocks of the Machine Learning Cicle.
Hypothesis Testing is the Scientific Method into practice. The method builds on experiments that will confirm or reject a specific hypothesis, with some level of confidence. The whole process is heavily based on Probability and Statistics, being one of the most important topics in the field of Statistical Inference. Hypothesis Testing is one the main tools Science provides to investigate causality.
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