Slope is Agnostic to Cause and Effect

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 Results of Social Learning

The Results of Social Learning

The results of Social learning are not always positive. We learn a lot from our friends, our culture, and the people around us that we are not always aware of. We are greatly influenced by what we see others doing and believing, and this includes the things we learn and come to believe as true facts about the world. This is easily demonstrated by polling the opinions of people who get their news from traditional news outlets relative to people who get their news from fringe sources with political biases. But it is also true in spaces you would not expect.

 

To describe problems in social learning results, Gerd Gigerezner in Risk Savvy writes, “All in all, social learning leads to a paradoxical result. In France, Germany, Italy, the United Kingdom, and the United States, doctors’ beliefs about diet and health – such as taking vitamin supplements or exercising – more closely resemble those of the general public in their country than of doctors in other countries.”

 

When it comes to general knowledge and an ability to distinguish between accurate information and fads, trends, or beliefs without evidence, we like to imagine that we are smart and capable of identifying the truth. We like to believe that our beliefs are based on reality, that we have carefully considered the facts, and that we hold our beliefs for good reason. We won’t admit that we believe the things we do because others hold those same beliefs, but as the doctor example above indicates, that is often the case. The Dartmouth Atlas Project shows differences across the USA in treatments for certain conditions and rates of diagnosis for different conditions. Some of that may be genetic and reflect real health differences across the country, but some of the differences reflect different treatment approach beliefs by doctors trained in and practicing in different regions of the country.

 

Social learning results are good when they bring people together in support of democratic norms or help people understand that sitting on a couch all day and eating pizza for dinner every night are unhealthy behaviors. However, social learning results can be negative when doctor’s group around wasteful medical practices. The results of social learning can also just be random and strange, such as when people fall into fad diets or exercise programs that have no discernable health benefits or harms. What we should take away from Gigerenzer’s quote is that our knowledge is not always as rock solid and evidence based as we would believe. We should be honest with ourselves and make an effort to investigate whether our beliefs are based on real evidence or based on the people in our social groups who happen to hold the same beliefs. Perhaps our beliefs are still justifiable after strict scrutiny, but perhaps some beliefs can be let go when we see they are based on little more than the opinions and feelings of people around us.
Quick Heuristics

Quick Heuristics

I really like the idea of heuristics. I have always thought of heuristics as short-cuts for problem solving or rules of thumb to apply to given situations to ease cognitive demand. We live in an incredibly complex world and the nature of reality cannot be deduced just by observing the world around us. For the world to get to the point where I can drink an espresso while listing to music streamed across the internet as I write a blog post, humanity collectively had to make discoveries involving microscopes, electromagnetism, and electricity, none of which were easily observable or intuitively understandable to our human ancestors.

 

To cope with a complex world and a limited ability to explore and understand that world, humans thrived through the use of heuristics. When faced with difficult problems and decisions, we substitute approximate but not exact answers. We can make a category judgement and reduce the number of decisions we have to make, taking a generalized path that will usually turn out well. Heuristics help us cope with the overwhelming complexity of the world, but they are not perfect, and they simplify the world according to the information we can observe and readily take in.

 

In Thinking Fast and Slow, Daniel Kahneman writes, “the heuristic answer is not necessarily simpler or more frugal than the original question – it is only more accessible, computed more quickly and easily. The heuristic answers are not random, and they are often approximately correct. And sometimes they are quite wrong.”

 

Heuristics are quick, which is important if you are foraging and hear a dangerous sound, if you need to pick a quick place for shelter as a storm approaches, or if you have to make quick decisions about how to behave in a small tribal group. The more fluidly and quicker a heuristic comes to mind, the more natural it will feel and the stronger people will grasp it, even if it is not true. Stories and myths contain relatable elements and extend common experiences to complex problems like how to govern an empire, understanding why storms occur, and guiding us as to how we should organize an economy. Heuristics give us short-cuts to understanding these complexities, but they are biased toward our accessible world and experiences, which means they only approximate reality, and cannot fully and accurately answer our questions. While they can get some concepts more or less correct and give us good approaches to life in general, they can also be very wrong with serious consequences for many people over many generations.
Luck & Success - Joe Abittan

Luck & Success

I am someone who believes that we can all learn from the lessons of others. I believe that we can read books, listen to podcasts, watch documentaries, and receive guidance from good managers and mentors that will help us learn, grow, and become better versions of ourselves. I read Good to Great and Built to Last from Jim Collins, and I have seen value in books that look at successful companies and individuals. I have  believed that these books offer insights and lessons that can help me and others improve and adopt strategies and approaches that will help us become more efficient and productive overtime to reach large, sustainable goals.

 

But I might be wrong. In Thinking Fast and Slow, Daniel Kahneman directly calls into question whether books form authors like Jim Collins are useful for us at all. The problem, as Kahneman sees it, is that such books fail to account for randomness and chance. They fail to recognize the halo effect and see patterns where none truly exist. They ascribe causal mechanisms to randomness, and as a result, we derive a lesson that doesn’t really fit the actual world.

 

Kahneman writes, “because luck plays a large role, the quality of leadership and management practices cannot be inferred reliably from observations of success.” Taking a group of 20 successful companies and looking for shared operations, management styles, leadership traits, and corporate cultures will inevitably end up with us identifying commonalities. The mistake is taking those commonalities and then ascribing a causal link between these shared practices or traits and the success of companies or individuals. Without randomized controlled trials, and without natural experiments, we really cannot identify a strong causal link, and we might just be picking up on random chance within our sample selection, at least as Kahneman would argue.

 

I read Good to Great and I think there is a good chance that Kahneman is correct to a large extent. Circuit City was one of the success stories that Collins touted in the book, but the company barely survived another 10 years after the book’s initial publication. Clearly there are commonalities identified in books like Good to Great that are no more than chance, or that might themselves be artifacts of good luck. Perhaps randomness from good timing, fortunate economic conditions, or inexplicably poor decisions by the competition contribute to any given company or individual success just as much as the factors we identify by studying a group of success stories.

 

If this is the case, then there is not much to learn from case studies of several successful companies. Looking for commonalities among successful individuals and successful companies might just be an exercise in random pattern recognition, not anything specific that we can learn from. This doesn’t fit the reality that I want, but it may be the reality of the world we inhabit. Personally, I will still look to authors like Jim Collins and try to learn lessons that I can apply in my own life and career to help me improve the work I do. Perhaps I don’t have to fully implement everything mentioned in business books, but surely I can learn strategies that will fit my particular situation and needs, even if they are not broad panaceas to solve all productivity hang-ups in all times and places.

Keep What’s Meaningful

The last few weeks I have been wasting time with thing that are not meaningful. My time and attention have been eaten away by things that don’t add value to my life and leave me feeling slightly guilty.

 

This morning I recognized, when I took advantage of an extra 30 minutes in my schedule, of how important it is to keep valuable things in our lives by cutting out the wasteful things. The easy path through life is filled with distracting, quick, and ultimately meaningless parts and pieces. We stay up too late watching pointless tv. We oversleep and eat low nutrition and thoughtless breakfast foods. We purchase large houses and put up with long and wasteful commutes. We make decisions all along the way that we don’t realize sacrifice our time, attention, and ability to meaningfully contribute to the world.

 

These observations on how society pushes our lives lead me to reflect on our daily decisions. I believe we all need to think critically about what are the most important factors in our lives. From there, we can begin to consider the large overarching decisions that we make to shape our lives. Once those decisions have aligned with our core values, we can start to think about the million small decisions that we make each day. This will bring our lives into alignment with our core values and help us cut out things that do not bring us value. It will help us think about what is meaningful and what decisions will help us  build a meaningful, thoughtful, and fulfilling life. Without this approach we won’t be able to think about how we live and our life choices, and we will fill ourselves with meaningless distractions and wastes of time.

 

Looking back at quotes I have written about, a quote from Colin Wright in his book Becoming Who We Need To Be seems particularly fitting with these thoughts. He writes, “Pursuing what’s meaningful is important, but just as important is understanding why we’re pursuing what we’re pursuing and how we’re undertaking that pursuit. Pay attention to the why behind your actions, and the how and what become a lot easier to define and control.” Understanding that why helps us see what we need to do to get to a place where we can have a valuable impact on the world. Each of the daily actions that we can take become more clear when we understand our motivations and what we truly want to work toward. Thinking deeply about purpose and meaning gives us a sense of how to make the most out of the short time we have on this planet.