Heckman correction

Sometimes we want to see how one thing affects another thing, but we may not have all the information we need. For example, imagine we want to find out how education affects a person's income. But there are other things that affect income, such as where a person lives or their gender.

So how can we find out the real effect of education on income, without being affected by these other things?

That's where Heckman Correction comes in. It helps to correct for this problem, by looking at people who are similar in many ways except for the thing we're interested in (in this case, education).

Imagine we have a group of people who are all similar in terms of where they live, their gender, and their race, but they have different levels of education. We can then compare the incomes of these people, and see if education really does have an effect on income, without being confounded by other factors.

Heckman Correction also helps to correct for something called "sample selection bias". This happens when we only select certain people to be in our study, and those people may not be representative of the entire population.

For example, if we only look at people who have jobs, and we try to see how education affects their income, we may be missing out on people who don't have jobs. But if we use Heckman Correction, we can account for this and get a more accurate estimate of the effect of education on income.

Overall, Heckman Correction is a useful tool that helps us to better understand how one thing affects another thing, by correcting for other factors that may be influencing the outcome.