Casual and correlated relationship

Causal and non-causal relationships

Correlation and Causation: We experience the world in a time-oriented manner through cause and effect. First Lucy ate that white berry, then she became sick. A correlation is a measure or degree of relationship between two variables. A causal relation between two events exists if the occurrence of the first causes the . We've all heard in school that “correlation does not imply causation,” but what That is, you're making assumptions about the causal effects of.

For instance, higher mental stress can actually influence a person to smoke. Once again, we can reject hypothesis based on inverse causality.

Higher age leads to both, having kids and higher maturity levels. Causal relation does exist.

Difference between correlation and causation (video) | Khan Academy

We definitely know that inverse causality is not possible. Also alternate reasoning or mutual independence can be rejected. If you were able to answer all the 4 scenarios correctly, you are ready for the next concept. In case you got any of the scenario wrong, you probably need more practice on finding cause-effect pairs.

What are the keypoints in establishing causation? Sometimes X and Y might just be correlated and nothing else. In such cases we reject hypothesis based on mutual independence. In fields like pharma, it is very important to establish cause-effect pairs. An experiment is often defined as random assignment of observational units to different conditions, and conditions differ by the treatment of observational units.

Treatment is a generic term, which translates most easily in medical applications e. If we do not have the luxury to do a randomized experiment, we are forced to work on existing data sources. These events have already happened without any control. Hence, the selection is not random. Deriving out causality from Observational data is very difficult and non-conclusive. For a conclusive result on causality, we need to do randomized experiments.

Why are observational data not conclusive? We can never conclude individual cause-effect pair. There are multiple reason you might be asked to work on observational data instead of experiment data to establish causality.

First is, the cost involved to do these experiments. For instance, if your hypothesis is giving free I-phone to customers, this activity will have an incremental gain on sales of Mac.

Doing this experiment without knowing anything on causality can be an expensive proposal. Second is, not all experiments are allowed ethically. For instance, if you want to know whether smoking contributes to stress, you need to make normal people smoke, which is ethically not possible. In that case, how do we establish causality using observational data?

Understanding both the statistical terms is very important not only to make conclusions but more importantly, making correct conclusion at the end. In this blogpost we will understand why correlation does not imply causation. But what they mean actually by saying this? You will get a clear idea once we go through this blogpost. It does not tell us why and how behind the relationship but it just says the relationship exists. Correlation between Ice cream sales and sunglasses sold.

What’s the difference between Causality and Correlation?

As the sales of ice creams is increasing so do the sales of sunglasses. Causation takes a step further than correlation. It says any change in the value of one variable will cause a change in the value of another variable, which means one variable makes other to happen.

For example, the dog, Fido, barks when his tail wags but there's no reason to suspect that there's a causal relation between these events.

While these events often occur together There are many times when Fido's tail wags and he doesn't bark and there are times when Fido barks but doesn't wag his tail. Furthermore, we may suspect that there is some common cause for these events like Fido's excitement when his owner comes home.

Now that we can agree that these are cases of correlation without causation We can discuss two types of correlation, positive and negative.

• Correlation does not imply causation
• Why correlation does not imply causation?

In the next video we'll discuss how these types of correlations specifically relate to different types of causation.

But for now let's just introduce them. When events frequently occur together like in the examples above they are positively correlated. If two events are positively correlated Then when one event is present the others often present as well. In our first example it being a sunny day in Arizona is positively correlated with Andy succeeding on his math test.

On the other hand, two states are negatively correlated when it's likely that when one event occurs the other will not occur.

Logical Causation

For instance, when it snows, it's often not very sunny, so snowing and sunniness are negatively correlated. We often hear about positive and negative correlations, especially in the news. Taking vitamin C is positively correlated with recovering from the common cold more quickly than if one had not taken vitamin C.

Or headlines like "eating more nuts makes you less likely to have higher levels of bad cholesterol" indicates that eating more nuts is negatively correlated with having higher levels of bad cholesterol.

You may have heard headlines like these and had conversations with some friends about them and you may have heard someone say something like, "Awesome, so I'll just like eat more nuts and get rid of my bad cholesterol.

Unless you had evidence that a causal relation held it Is a mistake to suggest that this correlation is actually a causal relation. So it'd be wrong to say that eating more nuts will cause you to have lower levels of bad cholesterol, unless you have evidence that the causal relation held.

So let's consider an example where two events are positively correlated when neither causes the other. Consider this again, people with higher grades in college have higher grades in high school.