I have been thinking about the current direction of statistical research in astrology, and I would like to offer a different perspective.

First of all, I want to make one thing clear. I am not arguing against statistical studies. On the contrary, I think they are extremely valuable. Finding robust statistical correlations is an important first step. My concern is that many researchers seem to treat this step as the final goal, while I believe it should be the beginning of a much larger scientific program.

The reason is simple: correlation alone does not tell us why a relationship exists.

Imagine that we repeatedly find statistically significant correlations between planetary configurations and terrestrial events. The studies have high statistical power (>80%), very small p-values, and survive corrections for multiple testing. That is certainly interesting. But does it necessarily mean that planets influence events on Earth?

Not yet.

There is another explanation that must be taken seriously: temporal confounding.

Planetary positions are, fundamentally, functions of time. Many terrestrial processes are also functions of time. Economic cycles, social behavior, climate, epidemics, technological development, and countless other phenomena change over time. Therefore, it is entirely possible that both planetary configurations and observed events are independently correlated with time, creating an indirect association between them.

The statistical correlation itself cannot distinguish between these competing explanations.

It is compatible with at least several causal models:

• planets influence events;

• time influences both planetary positions and the observed events;

• some unknown third factor influences both;

• or the observed correlation is simply a statistical artifact.

Exactly the same dataset can support all of these possibilities. Correlation alone cannot tell us which one is correct.

This is why I believe the next step should not be searching for more correlations, but developing a fundamental causal model of astrological influence.

A scientific theory should not merely describe patterns. It should explain them.

Suppose, for example, we hypothesize that a planet's altitude above the horizon modifies the strength of its influence. That immediately generates a series of concrete, testable predictions. We would expect house position to matter. We could compare cases where zodiacal longitude is nearly identical but altitude above the horizon differs substantially. We could investigate whether the proposed effect changes continuously with altitude.

Notice what has happened. We have moved from asking, "Is there a correlation?" to asking, "Does reality behave as this mechanism predicts?"

That is a much stronger scientific question.

A causal model also dramatically reduces the search space.

Without a model, virtually every astrological technique becomes a candidate for investigation: signs, houses, aspects, midpoints, harmonics, fixed stars, asteroids, different house systems, Arabic Parts, and so on. The number of possible variables quickly becomes enormous. The research strategy naturally turns into searching through thousands—or millions—of possible combinations, hoping that some of them produce statistically significant results.

With a model, many of these possibilities can be excluded before any statistical analysis begins.

If the proposed mechanism depends on altitude, then house position becomes scientifically relevant, while midpoint structures may become secondary or even irrelevant. If the mechanism depends on angular separation, then aspects become central. The theory tells us what should matter before we analyze the data.

This is exactly how mature sciences operate.

Another important point is that good scientific theories generate new predictions.

A correlation explains existing data. A mechanism predicts observations that have not yet been made.

The history of science is full of examples. Kepler described planetary motion remarkably well, but Newton transformed those descriptions into a theory by proposing a mechanism. Mendel described inheritance patterns, but molecular genetics eventually explained why those patterns existed. Correlations often come first—but they are rarely the end of the scientific process.

There is also a practical reason for moving in this direction.

The broader scientific community is unlikely to take astrological research seriously if it consists primarily of increasingly sophisticated correlation studies while the possibility of temporal confounding remains unresolved. Researchers from statistics, causal inference, physics, neuroscience, or systems science are much more likely to engage if they see an attempt to formulate explicit causal hypotheses that can potentially be falsified.

Finally, I think it is worth remembering one fundamental distinction.

Correlation answers the question:

"Are these variables associated?"

Science ultimately wants to answer a different question:

"Why are they associated?"

Until we can propose—and rigorously test—a plausible causal mechanism, every observed correlation remains compatible with multiple competing explanations. Statistical evidence is necessary, but it is not sufficient.

For that reason, I believe the future of astrological research lies not in discovering ever more correlations, but in building and testing explicit models of the underlying process. Once we have such models, statistics becomes far more powerful, because it is no longer searching blindly—it is evaluating specific theoretical predictions.

To me, that is the path that has the greatest chance of turning astrology from a collection of intriguing statistical observations into a genuine scientific research program.