Understanding of how changes in one variable might affect the others can be one of the most important and helpful purposes of any statistical study. One of the first things that comes to mind while facing such problems might be analyzing the correlation between two variables; but as most students might have heard: “Correlation does not imply causation.” and we cannot really draw conclusions about causes and effects for any phenomenon using that approach. Then if so, what does imply causation? This would be what we are going to talk about in this lecture by using and introducing some main elements of Causal Inference and discussing some examples.