As humans, we are hardwired to look for patterns and identify relationships between things we observe in the world around us. Our brains naturally tend to fill in details and come up with explanations for these relationships. Unfortunately, jumping to conclusions often leads us to see nonrandom in the random and to blur the lines between association and causation.

Before we get some hands-on experience using causal inference methods, we need to build up some intuition about what exactly causal inference is, when causal inference is appropriate to use, and what causal inference can (and cannot) do. In this lesson, we will gradually introduce you to the key ideas, statistical frameworks, and required assumptions that are fundamental to causal inference.


Take a look at the comic strip to the right. Are you confident you know the difference between association and causation?

Source: xkcd

Take this course for free

Mini Info Outline Icon
By signing up for Codecademy, you agree to Codecademy's Terms of Service & Privacy Policy.

Or sign up using:

Already have an account?