What Are the Three Criteria for Causality?

Understanding the process, how things work, how one event leads to the other, or the famous concept of the butterfly effect, all these ideas relate to causality in one way or another. The word causality generically means cause and effect, how one happening recognized and identified as a cause inherently leads to a happening referred to as an effect. The term causal analysis is also established from this word.

Causality is a fundamental concept widely used in many genres to make accurate and robust predictions from data to make important decisions. In this article, we’ll discuss the three criteria for causality that are used to determine the extent to which we can accurately determine whether a particular cause can be considered responsible for a relevant effect – hence establishing a causal relationship. So, let’s get going!

Causality – What is it?

Causal inference is a field of study in statistics and data science that deals with understanding the causal relationships between different variables. In essence, it seeks to answer the question: “What causes what?”.

Causal inference has a wide range of applications in many industries such as business, science, and medicine. For example, causal inference is used to determine the effectiveness of treatments the medical industry. Researchers conduct randomized controlled trials and use causal inference to compare the outcomes of a treatment group with those of a control group to determine whether the treatment is effective or not. Without causal inference however, it would become quite hard to determine whether a treatment is truly effective or it’s just that the observed outcomes are simply due to chance.

So, it’s quite important to not mistake chance or correlation with causal relationships, which can sometimes become harder than it sounds. So, let’s discuss the three most important criteria to decide if there exists a causal relationship, namely temporal precedence, covariation, and non-spuriousness:

  1. Temporal Precedence

One factor that determines the basis of causality is the phenomenon of temporal sequence. Generically, temporal sequence means establishing that a cause must occur before the event, hence identifying an appropriate time of order of the events (as from the word sequence). The two variables existing in the form of variables can be either dependent or independent and can live to determine whether a correlation can be identified between them.

Defining proportionality can be one added perspective of explaining the temporal sequence relative to causal analysis, i.e., correctly determining the relationship between two undertaking events.

For example, it is proven that smoking causes lung cancer: individuals who indulge in the habit of smoking, their risk of developing lung cancer increases, in contrast to individuals who quit smoking have a significantly reduced risk of developing lung cancer. Hence, a causal relationship between two variables can be identified, smoking as a causal factor. Furthermore, the temporal sequence of smoking preceding the development of lung cancer can also be determined.

  1. Covariation

The second criterion for causality, covariation, also known as concomitant variation, generically means defining or identifying a means of direct proportionality between the cause-and-effect variables. This can be understood that when we observe a change in the cause variable, a change in the effect variable should also be observed. The presence or absence of cause should also lead to the presence or absence of an effect.

One popular method to determine causality is through experimental research design. It includes the researcher manipulating the independent variable and observing its effects on the dependent variable. However, in most practical cases, it is not possible or ethical to manipulate variables in this way. In which case, researchers mostly rely on observational studies to investigate causality.

For example, it is understood that the risk of heart disease or heart failure is significantly increased by the increase in the quantity of red blood cells. In this causal relationship, quantity of red blood cells is identified as the inherent cause, and the risk of heart failure can be identified as an effect. The change in one variable, i.e., the increase in red blood cells (cause), causes a difference in the other variable, the increased risk of heart failure (effect); hence covariation can be identified between the variables.

Another example can be diabetes and obesity, the relationship between them can be identified as that obesity which acts as the cause, may lead to diabetes which is the observed effect. Hence a covariation can be placed between the variables.

  1. Non-spuriousness

In the causal effect relationship, the existence of two variables is predeterminant, and their correlation, i.e., their relationship, should be unaffected by outside noise or changes. In simple terms, we need to rule out the possibility that a third variable can explain the cause-and-effect relationship, i.e., the association is a mere coincidence or result of some other factor that is not being considered. This whole phenomenon is known as non-spuriousness or elimination of spurious correlations, the third criterion of causality. 

For example, understanding the relationship between exercise and heart health. Regular exercise leads to better and improved heart health, reducing the risk of heart disease. However, other variables should also be considered to determine a non-spurious relationship.

Individuals who exercise daily may also follow other healthy lifestyle options, such as getting better sleep and eating healthy food. These factors may be adjusted in the original determination, further improving the causal research’s predictability and accuracy, therefore establishing causality.

Another example can be that when it rains heavily, the number of deaths occurring increases. These factors seem related to each other but a third variable which can be considered is that rain causes flooding which may lead to people dying from electric shock caused by exposed wiring. Considering these outside factors or third variables ultimately increases the accuracy of the research conducted.

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Wrap Up

Understanding causality is vital in many fields. It is a complex process that involves identifying the relationship between two variables, ensuring temporal sequence, covariation, and non-spuriousness to understand better and utilize the data at hand and identifying the cause and effect between variables.

While these are the three most important criteria for causality and they provide a solid foundation for establishing causality, additional criteria can also be used such as strength of association, reversibility, consistency, plausibility, and specificity to further strengthen the evidence. These criteria help to understand better how the data can be interpreted to make the best use possible and get robust and accurate models.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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