Bu uzun yazıyı çevirmeden paylaşmak zorunda olduğum için üzgünüm.
Günümüzde her gün binlerce kez maruz kaldığımız yanlış sebep-sonuç ilişkilerinin
örneklerini veriyor yazar, liderleri bu konuda duyarlı olmaya çağırıyor. En güncel örnek,
ülkemizdeki yüksek faizin enflasyona yol açması iddiasıdır mesela. Dua ile yağmur yağdırma
ise sözüm ona büyücülerin özelikle orta çağda sıkça kullandığı bir numaradır. İki olgunun
aynı zaman parçasında veya birbiri ardı sıra ortaya çıkması her şartta birbirinin nedeni
olduğunu göstermez. Birbirinin nedeni olduğu durumlarda, sebeplerle sonuçlar sıkça
birbirine karıştırılır. Esas sebep aslında başka bir etkendir.
Düşünme ve anlama yeteneğimizi dumura uğratır yanlış sebep sonuç ilişkilerine yaslanmak.
İnsanın düşünen bir hayvan olduğu tezini çürütecek kadar yaygın bir sorun haline gelmiştir.
Gerçeğin önemsizleştirilmesi denen cehenneme giden çiçekli yol hatalı çıkarsama ve
düşünme taşları ile döşenmiştir.
……
Leaders: Stop Confusing Correlation with Causation
by Michael Luca
We’ve all been told that correlation does not imply causation. Yet many business leaders,
elected officials, and media outlets still make causal claims based on misleading
correlations. These claims are too often unscrutinized, amplified, and mistakenly used to
guide decisions.
Examples abound: Consider a recent health study that set out to understand whether taking
baths can reduce the risk of cardiovascular disease. The analysis found that people who took
baths regularly were less likely to have cardiovascular disease or suffer strokes. The authors
conclude that the data suggests “a beneficial effect” of baths. Without a controlled
experiment, or a natural experiment, one in which subjects are chosen randomly and
without variable manipulation, it’s hard to know whether this relationship is causal. For
example, it’s possible that regular bath takers are generally less stressed and have more
free time to relax, which could be the real reason they have lower rates of heart disease.
Still, these findings were widely circulated, with headlines like, “Taking a bath isn’t just
relaxing. It could also be good for your heart.”
A large body of research in behavioral economics and psychology has highlighted systematic
mistakes we can make when looking at data. We tend to seek evidence that confirms our
preconceived notions and ignore data that might go against our hypotheses. We neglect important aspects of the way that data was generated. More broadly, it’s easy to focus on
the data in front of you, even when the most important data is missing. As Nobel Laureate
Daniel Kahneman has said, it can be as if “what you see is all there is.”
This can lead to mistakes and avoidable disasters, whether it’s an individual, a company, or a
government that’s making the decision. The world is increasingly filled with data, and we are
regularly bombarded with facts and figures. We must learn to analyze data and assess
causal claims — a skill that is increasingly important for business and government leaders.
One way to accomplish this is by emphasizing the value of experiments in organizations.
How Unsupported Causal Claims Lead Organizations Astray
A 2020 Washington Post article examined the correlation between police spending and
crime. It concluded that, “A review of spending on state and local police over the past 60
years…shows no correlation nationally between spending and crime rates.” This correlation
is misleading. An important driver of police spending is the current level of crime, which
creates a chicken and egg scenario. Causal research has, in fact, shown that more police lead
to a reduction in crime.
In 2013, eBay was spending roughly $50 million dollars per year advertising on search
engines. An analysis from consultants had shown that in areas where more advertisements
were shown, sales were higher. However, economists Tom Blake, Chris Nosko, and Steve
Tadelis pushed the company to think more critically about the causal claim. They analyzed
natural experiments and conducted a new randomized controlled trial, and found that these
ads were largely a waste, despite what the marketing team previously believed. The
advertisements were targeting people who were already likely to shop on eBay.
The targeted customers’ pre-existing purchase intentions were responsible for both the
advertisements being shown and the purchase decisions. eBay’s marketing team made the
mistake of underappreciating this factor, and instead assuming that the observed
correlation was a result of advertisements causing purchases. Had eBay explored other
factors that may have been responsible for the correlation, they would likely have avoided
the mistake.
Yelp overcame a similar challenge in 2015. A consulting report found that companies that
advertised on the platform ended up earning more business through Yelp than those that
didn’t advertise on the platform. But here’s the problem: Companies that get more business
through Yelp may be more likely to advertise. The former COO and I discussed this challenge
and we decided to run a large-scale experiment that gave packages of advertisements to
thousands of randomly selected businesses. The key to successfully executing this
experiment was determining which factors were driving the correlation. We found that Yelp
ads did have a positive effect on sales, and it provided Yelp with new insight into the effect
of ads.
The Nobel Committee Champions Causal Inference Research
The landscape of empirical economics has dramatically changed over the past forty years.
The field of economics has developed a set of skills that focus on assessing causal
relationships. Two out of the last three Nobel Prizes have been awarded for this work. Two
years ago, Abhijit Banerjee, Esther Duflo, and Michael Kremer shared the Nobel Prize for “for
their experimental approach to alleviating global poverty.” This year, economists Josh
Angrist, Guido Imbens, and David Card won the Nobel Prize for spearheading what Angrist
dubbed the “credibility revolution” within economics. The committee praised Angrist and
Imbens for “their methodological contributions to the analysis of causal relationships,” and
Card for “his empirical contributions to labour economics.” They are pioneers in natural
experiment research.
The development of the causal inference toolkit has been remarkable, and the work of the
Nobel recipients is truly inspiring. But you don’t need to be a PhD economist to think more
carefully about causal claims. A good starting place is to take the time to understand the
process that is generating the data you are looking at. Rather than assuming a correlation
reflects causation (or that a lack of correlation reflects a lack of causation), ask yourself
what different factors might be driving the correlation — and whether and how these might
be biasing the relationship you are seeing. In some cases, you’ll come out feeling reassured
that the relationship is likely causal. In others, you might decide not to trust the finding.
If you are worried that a correlation might not be causal, experiments can be a good starting
point. Companies such as Amazon and Booking.com put experiments at the heart of their
decision-making process. But experiments are not always feasible. In these cases, you
should think about — and seek out — other evidence that might shed light on the question
you are asking. In some cases, you might even find a good natural experiment of your own.
