Discuss and compare the difference between causal inference and statistical inference.

Discuss and compare the difference between causal inference and statistical inference.
From textbook:“Causality is at the heart of epidemiology because protect and control modes, rather than reactions to a crisis, can be implemented. However, the concept of causality is controversial. In exploring the cause of diseases, “sufficient cause,” a term that involves a combination of several factors or observations that have been “connected” to a certain disease, needs to be determined. This is especially true when we talk about chronic diseases and risk factors. Risk factors, also known as predisposing factors, increase the probability of disease and, although not sufficient to cause a disease, their presence increases the chance of developing a disorder. At-risk behaviors occur when healthy persons engage in behaviors that put them at risk for disease. Predisposing factors, however, produce susceptibility in the host without causing a disease. Casual inference is a conclusion about the cause of a health-related event. The effects of environmental factors and physical health may be perceived as causal; however, according to Rothman and Greenland (2005), causal effects cannot be proven. Based on past experiences, we make inferences and have expectations every day. For instance, when we turn on the light switch, a light comes on. But is it this action alone that causes the light to come on?In science, hypotheses are expectations, results are the predictions or experiences, and observations become data. Scientists apply formal methods using statistical inference to make conclusions based upon a sample of a specific population. Causal inference, however, uses a list of criteria applied to study results.”