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Causality in its simplest form is the relationship between cause and effect. In science, one's main objective is to find causal relationships, or in simpler terms "This causes that". All of modern science is based on causal relationships and they are the core pillar of good science. The old mantra "correlation does not imply causation." is often what separates the science from the pseudoscience, the scientists from the cranks, the medicine from the alternative medicine, and much much more.
 Causal Inference
Causal inference is the process in which someone can use data to claim there is a causal relationship. This is central to most of science, and it is literally science at its core. Some people seem to forget about the part where the data has to support a causal relationship and not just a correlation between the data points. Causal inference is often very important in statistical data, as you are taking a large pre-existing dataset to come to a conclusion, and not a controlled test environment. Therefore it is much easier to use causal inference in these scenarios.
 The Bradford-Hill Criteria
In epidemiology, causal relationships can be determined via the Bradford-Hill Criteria. There are 8 parts of this criteria, each one strengthens the possibility of causal relationship between the cause of the disease and the effects of it. The criteria are as follows:
- Strength of the association — The stronger the association the more causal the outcome.
- Consistency of findings — Finding must apply in different conditions.
- Specificity of findings — There must be a one-to-one ratio of cause and effect.
- Temporal Sequence of association — The cause must precede the effect.
- Biological Gradient — The more exposure, the higher the disease rate.
- Biological Plausibility — Is there a biological mechanism for what is happening?
- Coherence — Does it match up with what is already known about the disease?
- Experiment — Does removal of exposure change the outcome?