Father and son heights are an example of regression to the mean in this book. Above average height father will tend to have a son who is shorter, while still above average. 

Conversely, an above average height son will tend to have a father who is also above average, but shorter. 

While this seems like a contradiction, these are tendencies across an entire population that will always trend toward the mean height.

This reversability of prediction in regression to the mean is why there is no difference between cause and effect in this situaton.


The Book of Why: Regression to the Mean

Regression to the mean can be explained by a correlation coefficient with the absolute value smaller than 1.

Regression to the mean explained by correlation

While using a Galton board to present to inheritance, on the board on the left, Francis Galton noticed that the balls piled up in a normal distribution or a bell-shaped curve. In the board, in the middle, the A and B were first thought as different generations with the distribution widened each time through them. The board on the right had chutes to move the distribution closer together with his explanation for regression to the mean.

Francis Galton ended discovering a statistics law instead of a hereditary. Galton thought that regression to the mean was a casual process but eventually realized that statistics and causation were separate.

Origins of Regression to the Mean  

Regression to the mean acts as a severe threat to internal validity when participants are selected for a research study specifically because of their extreme initial scores. Statistically, individuals who achieve exceptionally high or low scores on a pretest are highly likely to score closer to the population average on a subsequent posttest simply due to natural, random variability. This makes it incredibly difficult to determine whether a shift in scores was genuinely caused by the experimental treatment or was merely the result of statistical regression.

Regression to the Mean as a Threat to Internal Validity

Which of the following best describes the statistical phenomenon of regression to the mean?

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.

University of Michigan - Ann Arbor

Google

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

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

The Book of Why

https://kpu.pressbooks.pub/psychmethods4e/

KPU Research Methods in Psychology - 4th American Edition

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 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 

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.

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.

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