## Alternative, Nonexistent Worlds

Judea Pearl’s The Book of Why hinges on a unique ability that human animals have. Our ability to imagine alternative, nonexistent worlds is what has set us on new pathways and allowed us to dominate the planet. We can think of what would happen if we acted in a certain manner, used a tool in a new way, or if two objects collided together. We can visualize future outcomes of our actions and of the actions of other bodies and predict what can be done to create desired future outcomes.
In the book he writes, “our ability to conceive of alternative, nonexistent worlds separated us from our protohuman ancestors and indeed from any other creature on the planet. Every other creature can see what is. Our gift, which may sometimes be a curse, is that we can see what might have been.”
Pearl argues that our ability to see different possibilities, to imagine new worlds, and to be able to predict actions and behaviors that would realize that imagined world is not something we should ignore. He argues that this ability allows us to move beyond correlations, beyond statistical regressions, and into a world where our causal thinking helps drive our advancement toward the worlds we want.
It is important to note that he is not advocating for holding a belief and setting out to prove it with data and science, but rather than we use data and science combined with our ability to think causally to better understand the world. We do not have to be stuck in a state where we understand statistical techniques but deny plausible causal pathways. We can identify and define causal pathways, even if we cannot fully define causal mechanisms. Our ability to reason through alternative, nonexistent worlds is what allows us to think causally and apply this causal reasoning to statistical relationships. Doing so, Pearl argues, will save lives, help propel technological innovation, and will push science to new frontiers to improve life on our planet.

## Regression Coefficients

Statistical regression is a great thing. We can generate a scatter plot, generate a line of best fit, and measure how well that line describes the relationship between the individual points within the data. The better the line fits (the more that individual points stick close to the line) the better the line describes the relationships and trends in our data. However, this doesn’t mean that the regression coefficients tell us anything about causality. It is tempting to say that a causal relationship exists when we see a trend line with lots of tight fitting dots around and two different variables on an X and Y axis, but this can be misleading.
In The Book of Why Judea Pearl writes, “Regression coefficients, whether adjusted or not, are only statistical trends, conveying no causal information in themselves.” It is easy to forget this, even if you have had a statistics class and know that correlation does not imply causation. Humans are pattern recognition machines, but we go a step beyond simply recognizing a pattern, we instantly set about trying to understand what is causing the pattern. However, our regression coefficients and scatter plots don’t always hold clear causal information. Quite often there is a third hidden variable that cannot be measured directly that is influencing the relationship we discover in our regression coefficients.
Pearl continues, “sometimes a regression coefficient represents a causal effect, and sometimes it does not – and you can’t rely on the data alone to tell you the difference.” Imagine a graph with a regression line running through a plot of force applied by a hydraulic press and fracture rates for ceramic mugs. One axis may be pressure, and the other axis may be thickness of the ceramic mug. The individual points represent the point at which individual mugs fractured We would be able to generate a regression line by testing the fracture strength of mugs of different thickness, and from this line we would be able to develop pretty solid causal inferences about thickness and fracture rates. A clear causal link could be identified by the regression coefficients in this scenario.
However, we could also imagine a graph that plotted murder rates in European cities and the spread of Christianity. With one axis being the number of years a city has had a Catholic bishop and the other axis being the number of murders, we may find that murders decrease the longer a city has had a bishop.  From this, we might be tempted to say that Christianity (particularly the location of a Bishop in a town) reduces murder. But what would we point to as the causal mechanism? Would it be religious beliefs adopted by people interacting with the church? Would it be that marriage rules that limited polygamy ensured more men found wives and became less murderous as a result? Would it be that some divinity smiled upon the praying people and made them to be less murderous? A regression like the one I described above wouldn’t tell us anything about the causal mechanism in effect in this instance. Our causal-thinking minds, however, would still generate causal hypothesis, some of which would be reasonable but others less so (this example comes from the wonderful The WEIRDest People in the World by Joseph Henrich).
Regression coefficients can be helpful, but they are less helpful when we cannot understand the causal mechanisms at play. Understanding the causal mechanisms can help us better understand the relationship represented by the regression coefficients, but the coefficient itself only represents a relationship, not a causal structure. Approaching data and looking for trends doesn’t help us generate useful information. We must first have a sense of a potential causal mechanism, then examine the data to see if our proposed causal mechanism has support or not. This is how we can use data and find support for causal hypothesis within regression coefficients.

## Laboratory Proof

“If the standard of laboratory proof had been applied to scurvy,” writes Judea Pearl in The Book of Why, “then sailors would have continued dying right up until the 1930’s, because until the discovery of vitamin C, there was no laboratory proof that citrus fruits prevented scurvy.” Pearl’s quote shows that high scientific standards for definitive and exact causality are not always for the greater good. Sometimes modern science will spurn clear statistical relationships and evidence because statistical relationships alone cannot be counted on as concrete causal evidence. A clear answer will not be given because some marginal unknowns may still exist, and this can have its own costs.
Sailors did not know why or how citrus fruits prevented scurvy, but observations demonstrated that citrus fruits managed to prevent scurvy. There was no clear understanding of what scurvy was or why citrus fruits were helpful, but it was commonly understood that a causal relationship existed. People acted on these observations and lives were saved.
On two episodes, the Don’t Panic Geocast has talked about journal articles in the British Medical Journal that make the same point as Pearl. As a critique of the need for randomized controlled trials, the two journal articles highlight the troubling reality that there have not been any randomized controlled trials on the effectiveness of parachute usage when jumping from airplanes. The articles are hilarious and clearly satirical, but ultimately come to the same point that Pearl does with the quote above – laboratory proof is not always necessary, practical, or reasonable when lives are on the line.
Pearl argues that we can rely on our abilities to identify causality even without laboratory proof when we have sufficient statistical analysis and understanding of relationships. Statisticians always tell us that correlation is not causation and that observational studies are not sufficient to determine causality, yet the citrus fruit and parachute examples highlight that this mindset is not always appropriate. Sometimes more realistic and common sense understanding of causation – even if supported with just correlational relationships and statistics – are more important than laboratory proof.

## Slope is Agnostic to Cause and Effect

I like statistics. I like to think statistically, to recognize that there is a percent chance of one outcome that can be influenced by other factors. I enjoy looking at best fit lines, seeing that there are correlations between different variables, and seeing how trend-lines change if you control for different variables. However, statistics and trend lines don’t actually tell us anything about causality.
In The Book of Why Judea Pearl writes, “the slope (after scaling) is the same no matter whether you plot X against Y or Y against X. In other words, the slope is completely agnostic as to cause and effect. One variable could cause the other, or they could both be effects of a third cause; for the purpose of prediction, it does not matter.”
In statistics we all know that correlation is not causation, but this quote helps us remember important information when we see a statistical analysis and a plot with linear regression line running through it. The regression line is like the owl that Pearl had described earlier in the book. The owl is able to predict where a mouse is likely to be and able to predict which direction it will run, but the owl does not seem to know why a mouse is likely to be in a given location or why it is likely to run in one direction over another. It simply knows from experience and observation what a mouse is likely to do.
The regression line is a best fit for numerous observations, but it doesn’t tell us whether one variable causes another or whether both are influenced in a similar manner by another variable. The regression line knows where the mouse might be and where it might run, but it doesn’t know why.
In statistics courses we end at this point of correlation. We might look for other variables that are correlated or try to control for third variables to see if the relationship remains, but we never answer the question of causality, we never get to the why. Pearl thinks this is a limitation we do not need to put on ourselves. Humans, unlike owls, can understand causality, we can recognize the various reasons why a mouse might be hiding under a bush, and why it may chose to run in one direction rather than another. Correlations can help us start to see where relationships exist, but it is the ability of our mind to understand causal pathways that helps us determine causation.
Pearl argues that statisticians avoid these causal arguments out of caution, but that it only ends up creating more problems down the line. Important statistical research in areas of high interest or concern to law-makers, business people, or the general public are carried beyond the cautious bounds that causality-averse statisticians place on their work. Showing correlations without making an effort to understand the causality behind it makes scientific work vulnerable to the epistemically malevolent who would like to use correlations to their own ends. While statisticians rigorously train themselves to understand that correlation is not causation, the general public and those struck with motivated reasoning don’t hold themselves to the same standard. Leaving statistical analysis at the level of correlation means that others can attribute the cause and effect of their choice to the data, and the proposed causal pathways can be wildly inaccurate and even dangerous. Pearl suggests that statisticians and researchers are thus obligated to do more with causal structures, to round off  their work and better develop ideas of causation that can be defended once their work is beyond the world of academic journals.

## The Fundamental Nature of Cause and Effect

In my undergraduate and graduate studies I had a few statistics classes and I remember the challenge of learning probability. Probability, odds, and statistics are not always easy to understand and interpret. There are some concepts that are pretty straightforward, and others that seem to contradict what we would expect if we had not gone through the math and if we had not studied the concepts in depth. To contrast the difficult and sometimes counter-intuitive nature of statistics, we can think about causality, which is a challenging concept, but unlike statistics, is something we are able to intuit from very young age.
In The Book of Why Judea Pearl writes, “In both a cognitive and a philosophical sense, the idea of cause and effect is much more fundamental than probability. We begin learning causes and effects before we understand language and before we understand mathematics.”
As Pearl explains, we see causality naturally and experience causality as we move through our lives. From a young child who learns that if they cry they receive attention to a nuclear physicist who learns what happens when two atoms collide at high energy levels, our minds are constantly looking at the world and looking for causes. It begins by making observations of phenomena around us and continues as we predict what outcomes would happen based on certain system inputs. Eventually, our minds reach a point where we can understand why our predictions are accurate or inaccurate, and we can imagine new ways to bring about certain outcomes. Even if we cannot explain all of this, we can still understand causation at a fundamental and intuitive level.
However, many of us deny that we can see and understand the world in a causal way. I am personally guilty of thinking in a purely statistical way and ignoring the causal. The classes I took in college helped me understand statistics and probability, but also told me not to trust my intuitive causal thinking. Books like Kahneman’s Thinking Fast and Slow cemented this mindset for me. Rationality, we believe, requires that we think statistically and discount our intuitions for fear of bias. Modern science says we can only trust evidence when it is backed by randomized controlled trials and directs us to think of the world through correlations and statistical relationships, not through a lens of causality.
Pearl pushes back against this notion. By arguing that causality is fundamental to the human mind, he implies that our causal reasoning can and should be trusted. Throughout the book he demonstrates that a purely statistical way of thinking leaves us falling short of the knowledge we really need to improve the world. He demonstrates that complex tactics to remove variables from equations in statistical methods are often unnecessary, and that we can accept the results of experiments and interventions even when they are not fully randomized controlled trials.  For much of human history our causal thinking nature has lead us astray, but I think that Pearl argues that we have overcorrected in modern statistics and science, and that we need to return to our causal roots to move forward and solve problems that statistics tells us are impossible to solve.

## Predictions & Explanations

The human mind has incredible predictive abilities, but our explanatory abilities do not always turn out to be as equally incredible. Prediction is relatively easy when compared to explanation. Animals can predict where a food source will be without being able to explain how it got there. For most of human history our ancestors were able to predict that the sun would rise the next day without having any way of explaining why it would rise. Computer programs today can predict our next move in chess but few can explain their prediction or why we would make the choice that was predicted.
As Judea Pearl writes in The Book of Why, “Good predictions need not have good explanations. The owl can be a good hunter without understanding why the rat always goes from point A to point B.” Prediction is possible with statistics and good observations. With a large enough database, we can make a prediction about what percentage of individuals will have negative reactions to medications, we can predict when a traffic jam will occur, and we can predict how an animal will behave. What is harder, according to Pearl, is moving to the stage where we describe why we observe the relationships that statistics reveal.
Statistics alone cannot tell us why particular patterns emerge. Statistics cannot identify causal structures. As a result, we continually tell ourselves that correlation is not causation and that we can only determine what relationships are truly causal through randomized controlled trials. Pearl would argue that this is incorrect, and he would argue that this idea results from the fact that statistics is trying to answer a completely different question than causation. Approaching statistical questions from a causal lens may lead to inaccurate interpretations of data or “p-hacking” an academic term used to describe efforts to get the statistical results you wanted to see. The key is not hunting for causation within statistics, but understanding causation and supporting it through evidence uncovered via statistics.
Seeing the difference between causation and statistics is helpful when thinking about the world. Being stuck without a way to see and determine causation leads to situations like tobacco companies claiming that cigarettes don’t cause cancer or oil and gas companies claiming that humans don’t contribute to global warming. Causal thinking, however, utilizes our ability to develop explanations and applies those explanations to the world. Our ability to predict different outcomes based on different interventions helps us interpret and understand the data that the world produces. We may not see the exact picture in the data, but we can understand it and use it to help us make better decisions that will lead to more accurate causal understandings over time.

## Hope in Big Data

Most of us probably don’t work with huge data sets, but all of us contribute to huge data sets. We know the world of big data is out there, and we know people are working with big data, but there are not many of us who truly know what it means and how we should think about any of it. In The Book of Why, Judea Pearl argues that even many of those doing research and running companies based on big data don’t fully understand what it all means.
Pearl is critical of researchers and entrepreneurs who lack causal understandings but pursue new knowledge and information by pulling correlations and statistics out of large data sets. There are some companies that are taking advantage of the fact that huge amounts of computing power can give us insights into data sets that we never before could have generated, however, these insights are not always as meaningful as we are lead to believe.
Pearl writes, “The hope – and at present, it is usually a silent one – is that the data themselves will guide us to the right answers whenever causal questions come up.”
My last post was about the overuse of the phrase: correlation is not causation. Finding correlations and relationships in data is meaningless if we don’t also have causal understandings in mind. This is the critique that Pearl makes with the quote above. If we don’t have a way of understanding basic causal structures, then the phrase is right, correlations don’t mean anything. Many companies and researchers are in a stage where they are finding correlations and unexpected statistical results in big data, but they lack causal understandings to do anything meaningful with the data. In the world of public policy this feels like the saying, a solution in search of a problem or in the world of healthcare like a pay and chase scenario.
Pearl argues throughout the book that we are better at identifying causal structures than we are lead to believe in our statistics courses. He also argues that understanding causality is key to unlocking the potential of big data and actually getting something useful out of massive datasets. Without a grounding in causality, we are wasting our time with the statistical research we do. We are running around with solutions in the forms of big data correlations that don’t have a causal underpinning. It is as if we are paying fraudulent claims, then chasing down some of the money we spent and congratulating ourselves on preventing fraud. The end result is a poor use of data that we prop up as a magnanimous solution.

## Correlation and Causation

I have an XKCD comic taped to the door of my office. The comic is about the mantra of statistics, that correlation is not causation. I taped the comic to my office door because I loved learning statistics in graduate school and thinking deeply about associations and how mere correlations cannot be used to demonstrate that one thing causes another. Two events can correlate, but have nothing to do with each other, and a third thing may influence both, causing them to correlate without any causal link between the two things.
But Judea Pearl thinks that science and researchers have fallen into a trap laid out by statisticians and the infinitely repeated correlation does not imply causation mantra. Regarding this perspective of statistics he writes, “it tells us that correlation is not causation, but it does not tell us what causation is.”
Pearl seems to suggest in The Book of Why that there was a time where there was too much data, too much humans didn’t know, and too many people ready to offer incomplete assessments based on anecdote and incomplete information. From this time sprouted the idea that correlation does not imply causation. We started to see that statistics could describe relationships and that statistics could be used to pull apart entangled causal webs, identifying each individual component and assessing its contribution to a given outcome. However, as his quote shows, this approach never actually answered what causation is. It never actually told us when we can know and ascertain that a causal structure and causal mechanism is in place.
“Over and over again,” writes Pearl, “in science and in business, we see situations where mere data aren’t enough.”
To demonstrate the shortcomings of our high regard for statistics and our mantra that correlation is not causation, Pearl walks us through the congressional testimonies and trials of big tobacco companies in the United States. The data told us there was a correlation between smoking and lung cancer. There was overwhelming statistical evidence that smoking was related or associated with lung cancer, but we couldn’t attain 100% certainty just through statistics that smoking caused lung cancer. The companies themselves muddied the water with misleading studies and cherry picked results. They hid behind a veil that said that correlation was not causation, and hid behind the confusion around causation that statistics could never fully clarify.
Failing to develop a real sense of causation, failing to move beyond big data, and failing to get beyond statistical correlations can have real harms. We need to be able to recognize causation, even without relying on randomized controlled trials, and we need to be able to make decisions to save lives. The lesson of the comic taped to my door is helpful when we are trying to be scientific and accurate in our thinking, but it can also lead us astray when we fail to trust a causal structure that we can see, but can’t definitively prove via statistics.

In The Book of Why Judea Pearl argues that humans are better at modeling, predicting, and identifying causation than we like to acknowledge. For Pearl, the idea that we can see direct causation and study it scientifically is not a radical and naïve belief, but a common sense and defensible observation about human pattern recognition and intuition of causal structures in the world. He argues that we are overly reliant on statistical methods and randomized controlled trials that suggest relationships, but never tell us exactly what causal mechanisms are at the heart of such relationships.
One of the greatest frustrations for Pearl is the limitations he feels have been placed around ideas and concepts for causality. For Pearl, there is a sense that certain research, certain ways of talking about causality, and certain approaches to solving problems are taboo, and that he and other causality pioneers are unable to talk in a way that might lead to new scientific breakthroughs. Regarding a theory of causation and a the history of our study of causality, he writes, “they declared those questions off limits and turned to developing a thriving causality-free enterprise called statistics.”
Statistics doesn’t tell us a lot about causality. Statistical thinking is a difficult way for most people to think, and for non-statistically trained individuals it leads to frustrations. I remember around the time of the 2020 election that Nate Silver, a statistics wonk at posted a cartoon where one person was trying to explain the statistical chance of an outcome to another person. The other person interpreted statistical chances as either 50-50 or all or nothing. They interpreted a low probability event as a certainty that something would not happen and interpreted a high probability event as a certainty that it would happen, while more middle ground probabilities were simply lumped in as 50-50 chances. Statistics helps us understand these probabilities in terms of the outcomes we see, but doesn’t actually tell us anything about the why behind the statistical probabilities. That, I think Pearl would argue, is part of where the confusion for the individual in the cartoon who had trouble with statistics stems from.
Humans think causally, not statistically. However, our statistical studies and the accepted way of doing science pushes against our natural causal mindsets. This has helped us better understand the world in many ways, but Pearl argues that we have lost something along the way. He argues that we needed to be building better ways of thinking about causality and building models and theories of causality at the same time that we were building and improving our studies of statistics. Instead, statistics took over as the only responsible way to discuss relationships between events, with causality becoming taboo.
“When you prohibit speech,” Pearl writes, “you prohibit thought and stifle principles, methods, and tools.” Pearl argues that this is what is happening in terms of causal thinking relative to statistical thinking. I think he, and other academics who make similar speech prohibition arguments, are hyperbolic, but I think it is important to consider whether we are limiting speech and knowledge in an important way. In many studies, we cannot directly see the causal structure, and statistics does have ways of helping us better understand it, even if it cannot point to a causal element directly. Causal thinking alone can lead to errors in thinking, and can be hijacked by those who deliberately want to do harm by spreading lies and false information. Sometimes regressions and correlations hint at possible causal structures or completely eliminate others from consideration. The point is that statistics is still useful, but that it is something we should lean into as a tool to help us identify causality, not as the endpoint of research beyond which we cannot make any assumptions or conclusions.
Academics, such as Pearl and some genetic researchers, may want to push forward with ways of thinking that others consider taboo, and sometimes fail to adequately understand and address the concerns that individuals have about the fields. Addressing these areas requires tact and an ability to connect research in fields deemed off limits to the fields that are acceptable. Statistics and a turn away from a language of causality may have been a missed opportunity in scientific understanding, but it is important to recognize that human minds have posited impossible causal connections throughout history, and that we needed statistics to help demonstrate how impossible these causal chains were. If causality became taboo, it was at least partly because there were major epistemic problems in the field of causality. The time may have come for addressing causality more directly, but I am not convinced that Pearl is correct in arguing that there is a prohibition on speech around causality, at least not if the opportunity exists to tactfully and responsibly address causality as I think he does in his book.

## We Bet On Technology

I am currently reading Steven Pinker’s book Enlightenment Now and he makes a good case for being optimistic about human progress. In an age when it is popular to write about human failures, whether it is wealthy but unhappy athletes wrecking their cars, the perilous state of democracy, or impending climate doom, the responsible message always see ms to be warning about how bad things are. But Pinker argues that things are not that bad and that they are getting better. Pinker’s writing directly contradicts some earlier reading that I have done, including the writing of Gerd Gigerenzer who argues that we unwisely bet on technology to save us when we should be focused on improving statistical thinking and living with risk rather than hoping for a savior technology.
In Risk Savvy, Gigerenzer writes about the importance of statistical thinking and how we need it in order to successfully navigate an increasingly complex world. He argues that betting on technology will in some ways be a waste of money, and while I think he is correct in many ways, I think that some parts of his message are wrong. He argues that instead of betting on technology, we need to develop improved statistical understandings of risk to help us better adapt to our world and make smarter decisions with how we use and prioritize resources and attention. He writes, “In the twenty-first century Western world, we can expect to live longer than ever, meaning that cancer will become more prevalent as well. We deal with cancer like we deal with other crises: We bet on technology. … As we have seen … early detection of cancer is also of very limited benefit: It saves none or few lives while harming many.”
Gigerenzer is correct to state that to this point broad cancer screening has been of questionable use. We identify a lot of cancers that people would likely live with and that are unlikely to cause serious metastatic or life threatening disease. Treating cancers that won’t become problematic during the natural course of an individual’s life causes a lot of pain and suffering for no discernable benefit, but does this mean we shouldn’t bet on technology? I would argue that it does not, and that we can see the current mistakes we make with cancer screening and early detection as lessons to help us get to a better technological cancer detection and treatment landscape. Much of our resources directed toward cancer may be misplaced right now, but wise people like Gigerenzer can help the technology be redirected to where it can be the most beneficial. We can learn from poor decisions around treatment and diagnosis, call out the actors who profit from misinformation, uncertainty, and fear, and build a new regime that harnesses technological progress in the most efficient and effective ways. As Pinker would argue, we bet on technology because it offers real promises of an improved world. It won’t be an immediate success, and it will have red herrings and loose ends, but incrementalism is a good way to move forward, even if it is slow and feels like it is inadequate to meet the challenges we really face.
Ultimately, we should bet on technology and pursue progress to eliminate more suffering, improve knowledge and understanding, and better diagnose, treat, and understand cancer. Arguing that we haven’t done a good job so far, and that current technology and uses of technology haven’t had the life saving impact we wish they had is not a reason to abandon the pursuit. Improving our statistical thinking is critical, but betting on technology and improving statistical thinking go hand in hand and need to be developed together without prioritizing one over the other.