Tag: Causality

Talking About Causation - Judea Pearl - The Book of Why - Joe Abittan

Talking About Causation

Posted on by jabittan
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 Fivethirtyeight.com, 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.
Causal Links Between Unconnected Events

Causal Links Between Unconnected Events

Posted on by jabittan
As a kid I grew up attending basketball camps at UCLA. I played in the old gym that used to host UCLA games in front of a few thousand fans, played on the current court in main stadium, and slept in the dorms. With my history of basketball at UCLA, I have always been a fan of the men’s basketball team, rooting for them and the Nevada Wolf Pack – where I actually went to school. With the UCLA team making a deep run in the NCAA March Madness tournament, I have been reminded of all the superstitious thinking that surrounds sports and that I used to indulge in.
Sports seem to bring out superstitious thinking in even the most rational of people. I try very hard to think about causal structures and to avoid seeing non-existent causal links between unconnected events, but nevertheless, it is hard to not let superstitious thinking creep in. When you are watching a game it is hard not to feel like you have to sit in the right spot, have to watch from a certain room, or have to avoid certain behaviors in order to keep your team in the lead. However, it is absolute nonsense to think that your actions on your couch, miles away from the sporting venue where the game is taking place, could have any causal link to the way that a sports team performs.
In the book Vices of the Mind, Quassim Cassam spends time examining what is happening within our mind when we engage in superstitious thinking. He explains that superstitious thinking qualifies as an epistemic vice because it gets in the way of knowledge. It prevents us from forming accurate beliefs about the world. “Superstitious thinking,” Cassam writes, “isn’t a generally reliable method for forming true beliefs about the future; it won’t generally lead to true beliefs because it posits causal links between unconnected events. … beliefs based on superstitious thinking aren’t reasonable.”
Cassam gives the example of superstitions about walking under ladders in the book. Someone with a superstition believing that bad luck will befall them if they walk under a ladder will probably avoid walking under ladders, and as a result they won’t be as likely to have paint drip on them, to have something fall on their head, or to knock over the ladder and anyone or anything on top of it. Their superstition will lead to better outcomes for them, but not because the superstition helped them create true beliefs about the dangers of walking under ladders. The individual ends up with the correct answer, but interprets the wrong causal chain to get there.
Thinking about rational and plausible causal chains is a way to escape superstitious thinking. You can rationally examine the risks, harms, and benefits of certain behaviors and actions with rational connections between events to see when a superstition is nonsense, and when it pulls from real-life causal chains to help improve life. Trying not step on cracks will not prevent you from starting a causal chain that leads to your mother’s broken back, but it will help ensure you have more stable and steady footing when you walk. Wearing the same basketball jersey for each sports game has no causal connection with the team’s performance, and wearing it or not wearing it will not have an impact on how your favorite team performs. We should strive to have accurate beliefs about the world, we should work to see causal connections clearly, and we should limit superstitious thinking even if it is about trivial things like sports.
Explanatorily Basic

Explanatorily Basic

Posted on by jabittan
Quassim Cassam’s book Vices of the Mind is written more for an academic audience than a popular audience, and as a result it is rather dense and dives into some specific arguments with a lot of nuance. As an example, Cassam asks whether there is one type of epistemic vice that is more basic than another, or than any other, and takes the time to explain exactly what he means when he says that a vice might be more explanatorily basic than another.
Cassam writes, “A trait X is more basic than another trait Y if X can be explained without reference to Y, but Y can’t be explained  without reference to X. In this case, X is explanatorily more basic than Y.”
Ultimately, Cassam doesn’t find any evidence that any given epistemic vice is more basic than another. Epistemic vices are something that we do, and we can characterize each epistemic vice by a patter of thought that contributes to a certain behaviors or traits that obstructs knowledge. To characterize someone with a trait that is defined by an epistemic vice is simply to say that they are someone who often engages in that pattern of thought. According to Cassam, all epistemic vices are things that we do regardless as to whether or not we would normally describe ourselves or others by a vice, and therefore there is no reason to think that one epistemic vice is more basic than another. They don’t refer to or explain each other, they instead reference patterns of behavior and thought that we can engage with regularly or in particular instances.
While this idea is a bit obscure and fairly complex to think through, I think it can be a helpful way to look at the world. I believe that systems thinking is important within organizations and within our general lives. If we observe problems or situations that could be better, we should look for solutions and new structures that would improve the problems we see. In order to do that well, we should have a way of identifying root causes. We should approach not just the symptoms of the problems we see, but approach the overall structure to understand what causes the negative things we wish to prevent or avoid. Cassam’s definition for what would make an epistemic vice more explanatorily basic than another is part of a systemic and structural approach to the kind of problem solving that I would advocate for.
A root cause should be more explanatorily basic than the negative aspects that flow from it. When approaching a problem or a decision, we should ask whether the things we are focused on can be explained directly, or if they can only be explained by reference to other factors. If we can explain them without having to reference other problems that contribute to them, then we may have identified the root cause that we are after. Making a change at that point should influence downstream actions and consequences, helping adjust the structure of the system that lead to the issue we want to solve.
Regression to the Mean Versus Causal Thinking

Regression to the Mean Versus Causal Thinking

Posted on by jabittan
Regression to the mean, the idea that there is an average outcome that can be expected and that overtime individual outliers from the average will revert back toward that average, is a boring phenomenon on its own. If you think about it in the context of driving to work and counting your red lights, you can see why it is a rather boring idea. If you normally hit 5 red lights, and one day you manage to get to work with just a single red light, you probably expect that the following day you won’t have as much luck with the lights, and will probably have more red lights than than your lucky one red light commute. Conversely, if you have a day where you manage to hit every possible red light, you would probably expect to have better traffic luck the next day and be somewhere closer to your average. This is regression to the mean. Simply because you had only one red or managed to hit every red one day doesn’t cause the next day’s traffic light stoppage to be any different, but you know you will probably have a more average count of reds versus greens – no causal explanation involved, just random traffic light luck.

 

But for some reason this idea is both fascinating and hard to grasp in other areas, especially if we think that we have some control of the outcome. In Thinking Fast and Slow, Daniel Kahneman helps explain why it is so difficult in some settings for us to accept regression to the mean, what is otherwise a rather boring concept. He writes,

 

“Our mind is strongly biased toward causal explanations and does not deal well with mere statistics. When our attention is called to an event, associative memory will look for its cause – more precisely, activation will automatically spread to any cause that is already stored in memory. Causal explanations will be evoked when regression is detected, but they will be wrong because the truth is that regression to the mean has an explanation but does not have a cause.”

 

Unless you truly believe that there is a god of traffic lights who rules over your morning commute, you probably don’t assign any causal mechanism to your luck with red lights. But when you are considering how well a professional golfer played on the second day of a tournament compared to the first day, or when you are considering whether intelligent women marry equally intelligent men, you are likely to have some causal idea that comes to mind. The golfer was more or less complacent on the second day – the highly intelligent women have to settle for less intelligent men because the highly intelligent men don’t want an intellectual equal. These are examples that Kahneman uses in the book and present plausible causal mechanisms, but as Kahneman shows, the more simple though boring answer is simply regression to the mean. A golfer who performs spectacularly on day one is likely to be less lucky on day two. A highly intelligent woman is likely to marry a man with intelligence closer to average just by statistical chance.

 

When regression to the mean violates our causal expectation it becomes an interesting and important concept. It reveals that our minds don’t simply observe an objective reality, they observe causal structures that fit with preexisting narratives. Our causal conclusions can be quite inaccurate, especially if they are influenced by biases and prejudices that are unwarranted. If we keep regression to the mean in mind, we might lose some of our exciting narratives, but our thinking will be more sound, and our judgments more clear.
Base Rates Joe Abittan

Base Rates

Posted on by jabittan
When we think about individual outcomes we usually think about independent causal structures. A car accident happened because a person was switching their Spotify playlist and accidently ran a red light. A person stole from a grocery store because they had poor moral character which came from a poor cultural upbringing. A build-up of electrical potential from the friction of two air masses rushing past each other caused a lightning strike.

 

When we think about larger systems and structures we usually think about more interconnected and somewhat random outcomes that we don’t necessarily observe on a case by case basis, but instead think about in terms of likelihoods and conditions which create the possibilities for a set of events and outcomes. Increasing technological capacity in smartphones with lagging technological capacity in vehicles created a tension for drivers who wanted to stream music while operating vehicles, increasing the chances of a driver error accident. A stronger US dollar made it more profitable for companies to employ workers in other countries, leading to a decline in manufacturing jobs in US cities and people stealing food as they lost their paychecks.  Earth’s tilt toward the sun led to a difference in the amount of solar energy that northern continental landmasses experienced, creating a temperature and atmospheric gradient which led to lightning producing storms and increased chances of lightning in a given region.

 

What I am trying to demonstrate in the two paragraphs above is a tension between thinking statistically versus thinking causally. It is easy to think causally on a case by case basis, and harder to move up the ladder to think about statistical likelihoods and larger outcomes over entire complex systems. Daniel Kahneman presents these two types of thought in his book Thinking Fast and Slow writing:

 

“Statistical base rates are facts about a population to which a case belongs, but they are not relevant to the individual case. Causal base rates change your view of how the individual case came to be.”

 

It is more satisfying for us to assign agency to a single individual than to consider that individual’s actions as being part of a large and complex system that will statistically produce a certain number of outcomes that we observe. We like easy causes, and dislike thinking about statistical likelihoods of different events.

 

“Statistical base rates are generally underweighted, and sometimes neglected altogether, when specific information about the case at hand is available.
Causal base rates are treated as information about the individual case and are easily combined with other case-specific information.”

 

The base rates that Kahneman describes can be thought of as the category or class to which we assign something. We can use different forms of base rates to support different views and opinions. Shifting the base rate from a statistical base rate to a causal base rate may change the way we think about whether a person is deserving of punishment, or aid, or indifference. It may change how we structure society, design roads, and conduct cost-benefit analyses for changing programs or technologies. Looking at the world through a limited causal base rate will give us a certain set of outcomes that might not generalize toward the rest of the world, and might cause us to make erroneous judgments about the best ways to organize ourselves to achieve the outcomes we want for society.
Cause and Chance

Cause and Chance

Posted on by jabittan
Recently I have written a lot about our mind’s tendency toward causal thinking, and how this tendency can sometimes get our minds in trouble. We make associations and predictions based on limited information and we are often influenced by biases that we are not aware of. Sometimes, our brains need to shift out of our causal framework and think in a more statistical manner, but we rarely seem to do this well.

 

In Thinking Fast and Slow, Daniel Kahneman writes, “The associative machinery seeks causes. The difficulty we have with statistical regularities is that they call for a different approach. Instead of focusing on how the event at hand came to be, the statistical view relates it to what could have happened instead. Nothing in particular caused it to be what it is – chance selected it from among its alternatives.”

 

This is hard for us to accept. We want there to be a reason for why one candidate won a toss-up election and the other lost. We want there to be a reason for why the tornado hit one neighborhood, and not the adjacent neighborhood. Our mind wants to find patterns, it wants to create associations between events, people, places, and things. It isn’t happy when there is a large amount of data, unknown variables, and some degree of randomness that can influence exactly what we observe.

 

Statistics, however, isn’t concerned with our need for intelligible causal structures. Statistics is fine with a coin flip coming up heads 9 times in a row, and the 10th flip still having a 50-50 shot of being heads.

 

Our minds don’t have the ability to hold multiple competing narratives at one time. In national conversations, we seem to want to split things into 2 camps (maybe this is just an artifact of the United States having a winner take all political system) where we have to sides to an argument and two ways of thinking and viewing the world. I tend to think in triads, and my writing often reflects that with me presenting a series of three examples of a phenomenon. When we need to hold 7, 15, or 100 different potential outcomes in our mind, we are easily overwhelmed. Accepting strange combinations that don’t fit with a simple this-or-that causal structure is hard for our minds, and in many cases being so nuanced is not very rewarding. We can generalize and make substitutions in these complex settings and usually do just fine. We can trick our selves to believing that we think statistically, even if we are really only justifying the causal structures and hypotheses that we want to be true.

 

However, sometimes, as in some elections, in understanding cancer risk, and making cost benefit analyses of traffic accidents for freeway construction, thinking statistically is important. We have to understand that there is a range of outcomes, and only so many predictions we can make. We can develop aids to help us think through these statistical decisions, but we have to recognize that our brains will struggle. We can understand our causal tendencies and desires, and recognize the difficulties of accepting statistical information to help set up structures to enable us to make better decisions.
Causal Versus Statistical Thinking

Causal Versus Statistical Thinking

Posted on by jabittan
Humans are naturally causal thinkers. We observe things happening in the world and begin to apply a causal reason to them, asking what could have led to the observation we made. We attribute intention and desire to people and things, and work out a narrative that explains why things happened the way they did.

 

The problem, however, is that we are prone to lots of mistakes when we think in this way. Especially when we start looking at situations that require statistical thinking. In his book Thinking Fast and Slow, Daniel Kahneman writes the following:

 

“The prominence of causal intuitions is a recurrent theme in this book because people are prone to apply causal thinking inappropriately, to situations that require statistical reasoning. Statistical thinking derives conclusions about individual cases from properties of categories and ensembles. Unfortunately, System 1 does not have the capability for this mode of reasoning; system 2 can learn to think statistically, but few people receive the necessary training.”

 

System 1 is our fast brain. It works quickly to identify associations and patters, but it doesn’t take in a comprehensive set of information and isn’t able to do much serious number crunching. System 2 is our slow brain, able to do the tough calculations, but limited to work on the set of data that System 1 is able to accumulate. Also, System 2 is only active for short periods of time, and only when we consciously make use of it.

 

This leads to our struggles with causal thinking. We have to take in a wide range of possibilities, categories, and ranges of combinations. We have to make predictions and understand that in some set of instances we will see one outcome, but in another set of circumstances we may see a different outcome. Statistical thinking doesn’t pin down a concrete answer the way our causal thinking likes. As a result, we reach conclusions based on incomplete considerations, we ignore some important pieces of information, and we assume that we are correct because our answer feels correct and satisfies some criteria. Thinking causally can be powerful and useful, but only if we fully understand the statistical dimensions at hand, and can fully think through the implications of the causal structures we are defining.
Seeing Causality

Seeing Causality

Posted on by jabittan
In Thinking Fast and Slow Daniel Kahneman describes how a Belgian psychologist changed the way that we understand our thinking in regard to causality. The traditional thinking held that we make observations about the world and come to understand causality through repeated exposure to phenomenological events. As Kahneman writes, “[Albert] Michotte [1945] had a different idea: he argued that we see causality just as directly as we see color.”

 

The argument from Michotte is that causality is an integral part of the human psyche. We think and understand the world through a causal lens. From the time we are infants, we interpret the world causally and we can see and understand causal links and connections in the things that happen around us. It is not through repeated experience and exposure that we learn to view an event as having a cause or as being the cause of another event. It is something we have within us from the beginning.

 

“We are evidently ready from birth to have impressions of causality, which do not depend on reasoning about patterns of causation.”

 

I try to remember this idea of our intuitive and automatic causal understanding of the world when I think about science and how I should relate to science. We go through a lot of effort to make sure that we are as clear as possible with our scientific thinking. We use randomized controlled trials (RCT) to test the accuracy of our hypothesis, but sometimes, an intensely rigorous scientific study isn’t necessary for us to make changes in our behavior based on simple scientific exploration via normal causal thinking. There are some times where we can trust our causal intuition, and without having to rely on an RCT for evidence. I don’t know where to draw the line between causal inferences that we can accept and those that need an RCT, but through honest self-awareness and reflection, we should be able to identify times when our causal interpretations demonstrate validity and are reasonably well insulated from our own self-interests.

 

The Don’t Panic Geocast has discussed two academic journal articles on the effectiveness of parachutes for preventing death when falling from an aircraft during the Fun Paper Friday segment of two episodes. The two papers, both published in the British Medical Journal, are satirical, but demonstrate an important point. We don’t need to conduct an RCT to determine whether using a parachute when jumping from a plane will be more effective at helping us survive the fall than not using a backpack. It is an extreme example, but it demonstrates that our minds can see and understand causality without always needing an experiment to confirm a causal link. In a more consequential example, we can trust our brains when they observe that smoking cigarettes has negative health consequences including increased likelihood of an individual developing lung cancer. An RCT to determine the exact nature and frequency of cancer development in smokers would certainly be helpful in building our scientific knowledge, but the scientific consensus around smoking and cancer should have been accepted much more readily than what it was. An RCT in this example would take years and would potentially be unethical or impossible. Tobacco companies obfuscated the science by taking advantage of the fact that an RCT in this case couldn’t be performed, and we failed to accept the causal link that our brains could see, but could not prove as definitively as we can prove something with an RCT. Nevertheless, we should have trusted our causal thinking brains, and accepted the intuitive answer.

 

We can’t always trust the causal conclusions that our mind reaches, but there are times where we should acknowledge that our brains think causally, and accept that the causal links that we intuit are accurate.