A classic example of a spurious correlation is the observed relationship between ice cream sales and crime rates. While data shows both increase concurrently, one does not cause the other. The association is more likely explained by a third, confounding variable: temperature. During warm weather, more people are outside, leading to increased social interactions and potential for crime. Simultaneously, warm weather drives up the demand for cool treats like ice cream. Therefore, the temperature independently influences both variables, creating a statistical correlation without a direct causal link.

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A correlation between variables indicates a statistical relationship, but it does not provide any evidence about causation, regardless of how strong that relationship may be. Just because two variables move together does not mean that a change in one causes a change in the other. In scientific research, the only definitive way to demonstrate a cause-and-effect relationship is by conducting a controlled experiment.

Correlation Does Not Imply Causation

A confounding variable is an extraneous variable that differs on average across levels of the independent variable, meaning it varies systematically alongside it. Because this variable differs systematically across conditions just like the independent variable, it provides a plausible alternative explanation for any observed difference in the dependent variable, making it difficult to establish a clear cause-and-effect relationship. For instance, if an extraneous variable like intelligence is substantially lower in one group than another, it becomes a confounder that confuses the results.

Confounding Variable

Pearl, J., & Mackenzie, D. (2018). $\mathit{The \ Book \ of \ Why: \ The \ New \ Science \ of \ Cause \ and \ Effect.}$ Basic Books.

The Book of Why

Spielman, R. M., Jenkins, W. J., & Lovett, M. D. (2020, April 22). Psychology (2nd ed.). OpenStax. https://openstax.org/books/psychology-2e/pages/1-introduction

OpenStax Psychology (2nd ed.) Textbook

Event A directly causes event B, without any hidden contributing factors driving them both.

- A is the cause, B is the effect. 
- A correlation coefficient of 1 usually accompanies a causal relationship because it means that A occuring positively influences/ensures that B occurs. 

Causal Relationship

Events A and B have no impact on the occurence of the other. They do not cause the other.

Neutral Relationship

Instead of event A causing event B, event B causes event A. Sometimes called the 'cart before the horse bias'. 
Eg : If there is a patient at risk for a heart disease that reduces their intake of fatty/unhealthy foods, but then still suffers a heart attack. Their healthier lifestyle cannot be held as a cause for their heart attack, because the reason they shifted to a healthier lifestyle was because they were at high risk for a heart attack.

Reverse Causal relationship

When two heterogeneous data sets are aggregated into one, false correlations between variables can arise due to the differences in populations. 

For example, the correlation between skull length and breadth is statistically significant when measured on all humans, but when separated between male and female, there was no significant correlation.

Spurious Correlation: Aggregated Data

Inheritance of fur color in guinea pigs does not follow Mendelian laws. Sewell Wright postulated that this was because of 'developmental factors' (*d* ). He tried to find *d* by solving mathematical equations where *d* and hereditary factors *h* were the unknowns.

Wright showed that knowing *d* and *h* helped predict correlations, and drew a path diagram that allowed him to identify the causes after knowing correlations.

Reference : Pearl, J., & Mackenzie, D. (2019). The Book of Why. Penguin

S Wright's Guinea Pigs and the "First link between Causality and Probability"

Regression towards the mean is a phenomenon that occurs when resampling a single random variable. 

The phenomenon arises when the first sample of the random variable appears extreme, while the second sample appears much closer to the mean. This result occurs when the first sample is extremely above and below the mean.

Regression to the Mean

Proposed by Hans Reichenbach, the common cause principal argues against correlation doesn’t imply causation. It states that if there are two variables I and J and they are correlated, then I causes J or J causes I or there is another variable X that precedes and causes both I and J. 

This theory is not true because it doesn't account for how the data is selected and appeals to our human nature to look for casual explanations whenever there is a pattern. This is an example of collider bias.

Common Cause Principal 

Probability is a concept that is largely tied more toward association than it is causation as it in itself does not address the second and third rungs of the ladder of causation (Intervention and Counterfactual Reasoning, respectively). Therefore, to say that "X raises the probability of Y" is faulty as suggesting that it "raises" the probability implies causation, which is not an aspect of probabilistic expressions. Therefore, the proper way to discuss probability according to Judea Pearl would be to state that "If we see X, then the probability of Y increases." This delineates the association without jumping to causation and leaves the causal mechanism open to either interpretation or further research. 

Irreducibility of Causation to Probabilities 

Example of Spurious Correlation: Ice Cream Sales and Crime Rates

Claims of causation based on correlational data are frequently and mistakenly made, particularly in advertisements and news stories. These sources may present a statistical relationship in a way that implies a cause-and-effect link, often to promote a product or a particular narrative. This is sometimes done by maximizing and perhaps overstating the positive aspects of the findings, without acknowledging other possible explanations for the observed correlation.

Misinterpretation of Correlation as Causation in Media

A researcher conducts a study across 100 cities and finds a strong positive correlation between the number of public libraries in a city and the city's annual crime rate. Based on this finding, which of the following conclusions is the most scientifically sound?

Media reports often erroneously imply causation from correlational data, misleading the public. For instance, a headline claiming that eating candy daily as a child leads to violent behavior later in life assumes a causal link based merely on a statistical association. This conclusion ignores potential third variables that could influence both childhood diet and adult behavior. True causal relationships cannot be established from such observational data; they require controlled experiments.

Example of Misinterpreting Correlation: Candy and Violence

The directionality problem is a fundamental reason why correlation does not imply causation. It occurs when two variables, $$X$$ and $$Y$$, are statistically related, but it is impossible to determine the causal direction of the effect. Because neither variable is manipulated by the researcher, one cannot confidently identify whether $$X$$ causes $$Y$$, or if $$Y$$ causes $$X$$. For example, a statistical relationship showing that people who exercise are happier could mean that exercising causes happiness, or conversely, that happiness gives people the energy and desire to exercise.

Directionality Problem

The third-variable problem is a primary reason why correlational research cannot establish causality. It occurs when a statistical relationship between two variables, $$X$$ and $$Y$$, is not due to a direct causal link between them, but rather because some unmeasured third variable, $$Z$$, independently causes both. This creates a spurious correlation. For instance, a statistical relationship between exercise and happiness might exist simply because a third variable, such as overall physical health, simultaneously causes people to exercise more and feel happier.

Third-Variable Problem

In scientific research, what is the only definitive way to demonstrate a cause-and-effect relationship between variables?

There are two general approaches to removing confounds: 
• Randomize participants between conditions of the experiment 
• Experimentally control for potential confounds

Two approaches to removing confounds

Based on the research finding described in the case study, identify a likely third variable that is influencing both the number of firefighters and the amount of property damage. Explain how this variable creates the misleading impression of a direct relationship between the two.

Analyzing a Research Finding

Based on the study described, identify a likely third variable that could be influencing both the number of books in the home and the child's grades, and explain how this variable could account for the observed correlation.

Interpreting Correlational Research

A between-subjects experiment is an experimental design approach wherein each participant is exposed to only a single level of the independent variable. This method differs fundamentally from within-subjects designs, as individuals are tested in just one specific condition rather than across all experimental variations.

Between-Subjects Experiment

In a hypothetical study evaluating the effect of mood on memory, participants in a positive mood condition might score higher on a memory task than those in a negative mood condition. However, if participants in the positive mood condition also possess substantially higher intelligence quotients (IQs) on average than those in the negative mood group, IQ acts as a confounding variable. This systematic difference provides an alternative explanation for the results, making it unclear whether the positive mood or the higher intelligence caused the superior memory performance.

Example of a Confounding Variable: Mood and Memory

While quasi-experimental research manipulates an independent variable before measuring a dependent variable, it still cannot definitively establish cause-and-effect relationships. The absence of random assignment or counterbalancing means researchers cannot confidently rule out confounding variables—pre-existing differences between conditions—as the true cause of any observed outcomes.

Causal Limitations of Quasi-Experimental Research

What type of variable is an extraneous factor that differs on average across levels of the independent variable, providing a plausible alternative explanation for any observed difference?

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