MATH 2565 Lecture Notes - Predictive Inference, Causal Inference, Sampling Frame
14 Mar 2014
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Causal inference is when you are focusing on what happens to a variable, y, when you change another variable, x. Predictive inference is different because it focuses on predicting observations and what is going to happen to a variable, y, using past observations and information. Causal inference is focused on an immediate change, whereas predictive inference focuses on future predictions. Causal inference is the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect. In a causal inference, one reasons to the conclusion that something is, or is likely to be, the cause of something else. For example quitting smoking will get you unhealthy. An advantage of causal inference can determine the cause and effect of a study. An example could be whether or not nicotine affects one"s driving ability (which it does) and also experiments are the only means by which cause and effect can be established.
