# Causation and causal relationship example

### Correlation and Causation

Difference between causality & correlation is explained with examples. Cause- effect, observational data & ways to establish difference is. The four approaches to causality include neo-Humean regularity, But modern science has produced many examples where causal relationships appear to be. Correlation and causation are terms which are mostly misunderstood Correlation does not mean causality or in our example, ice cream is not.

Example 2 Young children who sleep with the light on are much more likely to develop myopia in later life. Therefore, sleeping with the light on causes myopia. This is a scientific example that resulted from a study at the University of Pennsylvania Medical Center.

Published in the May 13, issue of Nature[7] the study received much coverage at the time in the popular press. It did find a strong link between parental myopia and the development of child myopia, also noting that myopic parents were more likely to leave a light on in their children's bedroom.

Example 3 As ice cream sales increase, the rate of drowning deaths increases sharply. Therefore, ice cream consumption causes drowning. This example fails to recognize the importance of time of year and temperature to ice cream sales. Ice cream is sold during the hot summer months at a much greater rate than during colder times, and it is during these hot summer months that people are more likely to engage in activities involving water, such as swimming.

### Difference Between Causality And Correlation? | Business Analytics Tool

The increased drowning deaths are simply caused by more exposure to water-based activities, not ice cream. The stated conclusion is false.

This suggests a possible "third variable" problem, however, when three such closely related measures are found, it further suggests that each may have bidirectional tendencies see " bidirectional variable ", abovebeing a cluster of correlated values each influencing one another to some extent.

Therefore, the simple conclusion above may be false. Example 5 Since the s, both the atmospheric CO2 level and obesity levels have increased sharply. Hence, atmospheric CO2 causes obesity.

## Correlation and Causation

Richer populations tend to eat more food and produce more CO2. Example 6 HDL "good" cholesterol is negatively correlated with incidence of heart attack. Therefore, taking medication to raise HDL decreases the chance of having a heart attack. Further research [14] has called this conclusion into question. Instead, it may be that other underlying factors, like genes, diet and exercise, affect both HDL levels and the likelihood of having a heart attack; it is possible that medicines may affect the directly measurable factor, HDL levels, without affecting the chance of heart attack.

A causes B, and B causes A[ edit ] Causality is not necessarily one-way; in a predator-prey relationshippredator numbers affect prey numbers, but prey numbers, i.

Another well-known example is that cyclists have a lower Body Mass Index than people who do not cycle. This is often explained by assuming that cycling increases physical activity levels and therefore decreases BMI.

### Statistical Language - Correlation and Causation

Is having kids a cause of attaining higher maturity levels? Is higher altitude a cause of lower temperature? Hence, I have tried to explain the aspects of causation and correlation in the simplest possible manner. Understanding of this concept is very essential if you want to keep your foundations strong in this analytics industry, where we now mostly work on black boxes.

13-2 Correlation and Causality

Here are the Answers: Causal relation does not exist. Hence, we have alternate reasoning issue in this case. We can reject hypothesis based on inverse causality. 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. 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?

• Causation and Correlation
• Correlation does not imply causation
• Causation and Explanation in Social Science

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? This is one of the most daunting challenges of public health professionals and pharmaceutical companies. In a controlled study, two groups of people who are comparable in almost every way are given two different sets of experiences such one group watching soap operas and the other game showsand the outcome is compared. If the two groups have substantially different outcomes, then the different experiences may have caused the different outcome.

There are obvious ethical limits to controlled studies: This is why epidemiological or observational studies are so important.

These are studies in which large groups of people are followed over time, and their behavior and outcome is also observed. In these studies, it is extremely difficult though sometimes still possible to tease out cause and effect, versus a mere correlation. This was the case with cigarette smoking, for example. At the time that scientists, industry trade groups, activists and individuals were debating whether the observed correlation between heavy cigarette smoking and lung cancer was causal or not, many other hypotheses were considered such as sleep deprivation or excessive drinking and each one dismissed as insufficiently describing the data.

When the stakes are high, people are much more likely to jump to causal conclusions. This seems to be doubly true when it comes to public suspicion about chemicals and environmental pollution. There has been a lot of publicity over the purported relationship between autism and vaccinations, for example.

As vaccination rates went up across the United States, so did autism. And if you splice the data in just the right wayit looks like some kids with autism have had more vaccinations. However, this correlation which has led many to conclude that vaccination causes autism has been widely dismissed by public health experts.

The rise in autism rates is likely to do with increased awareness and diagnosis, or one of many other possible factors that have changed over the past 50 years.

Language further contorts the distinction, as some media outlets use words that imply causality without saying it.