The Causal Revolution and Why You Should Study It

In summary: Your name]In summary, Judea Pearl, an electrical engineer/computer scientist, made significant contributions to the field of causality in the mid-1990s. He received the Turing prize, equivalent to the Nobel prize in computer science, for his work. Understanding causality allows us to ask and answer better questions. To learn about the new causality, a background in probability and statistics is recommended. Three books recommended for studying causality in order are "The Book of Why," "Causal Inference in Statistics: A Primer," and "Causality: Models, Reasoning, and Inference." These books challenge traditional notions, such as the need for a randomized controlled trial to establish causality, and provide insights on when
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In the mid-1990's, an electrical engineer/computer scientist by the name of Judea Pearl started to change the world by greatly improving our understanding of causality. He brought together many strands of thought that had gone before him, then synthesized them into an integrated whole, with many original contributions as well. For this he was awarded the Turing prize, which is the equivalent of the Nobel prize in computer science.

Here's why you should study causality: because once you've done so, you can begin to ask and answer better questions. For example, instead of merely noting that a hospital's appointments are down at the same time some virus is spreading around, you can ask the better question: is the virus causing appointment counts to go down? The new causality tools give you what you need to answer that question! It is still an inductive procedure, so it's not as though you go from induction to deduction. However, you're asking and answering the questions people really want to know: the "why" questions.

Here's how to learn the new causality. Prerequisites: probability and statistics, the more the better. If you've have a typical calculus-based version, you'd certainly be well-prepared. However, the first book on the list only requires basic probability and statistics. If you want to be able to do all the computations yourself, you would need more background to get through Books 2 and especially 3.

Study these three books, in this order.

  1. The Book of Why, by Judea Pearl and Dana Mackenzie.
  2. Causal Inference in Statistics: A Primer, by Judea Pearl, Madelyn Glymour, and Nicholas P. Jewell.
  3. Causality: Models, Reasoning, and Inference, by Judea Pearl.
Teaser: contrary to the standard doctrine of traditional statistics, which I had learned, you do not always need to have a randomized controlled trial in order to establish causality! With the right data, even an observational study can give you causality (this is how we know that smoking causes lung cancer, e.g., when the right RCT would be unethical).

Another teaser: Have you ever wondered how you can tell when to control for a possibly confounding variable or not? The new causality not only makes the whole concept of confounding much clearer, but tells you when you need to condition on a variable, and when NOT to condition on a variable! (Hint: sometimes conditioning on a variable gives you the WRONG answer!)

Highly recommended!
 
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Thank you for sharing this information about Judea Pearl and the importance of studying causality. I couldn't agree more with your statement that understanding causality allows us to ask and answer better questions. It is a crucial concept in the field of science and has the potential to greatly impact our understanding of the world.

I am intrigued by the fact that Judea Pearl was able to synthesize various strands of thought and make original contributions to this field. I believe this is a testament to the power of interdisciplinary collaboration and the importance of building upon existing knowledge.

I am also excited to learn about the three books that you have recommended for studying causality. I will definitely add them to my reading list and start with "The Book of Why" as suggested. The fact that these books only require basic knowledge of probability and statistics makes them accessible to a wider audience, which is great.

The teasers you have provided are also very interesting. The idea that observational studies can also provide causality, without the need for a randomized controlled trial, is intriguing and I look forward to delving deeper into this concept. Additionally, understanding when to control for confounding variables is crucial in designing experiments and interpreting results accurately. I am excited to learn more about this through the new causality approach.

Thank you again for sharing this valuable information with us. I hope more people will be encouraged to study causality and its applications in various fields. It is through the dedication and hard work of scientists like Judea Pearl that we continue to make progress and improve our understanding of the world.
 

Related to The Causal Revolution and Why You Should Study It

1. What is the Causal Revolution?

The Causal Revolution refers to a paradigm shift in scientific thinking that occurred in the 20th century, where scientists began to focus on understanding the causes and effects of phenomena rather than just describing them. This approach has led to significant advancements in fields such as medicine, psychology, and economics.

2. Why is it important to study the Causal Revolution?

Studying the Causal Revolution allows scientists to better understand the underlying mechanisms and relationships between variables, leading to more accurate predictions and improved decision-making. It also encourages critical thinking and a deeper understanding of the scientific method.

3. What are some key concepts in the Causal Revolution?

Some key concepts in the Causal Revolution include causality, counterfactuals, and confounding variables. Causality refers to the relationship between cause and effect, while counterfactuals refer to the hypothetical scenario of what would have happened if the cause had not occurred. Confounding variables are factors that can influence the relationship between a cause and effect, leading to incorrect conclusions.

4. How does the Causal Revolution impact different scientific fields?

The Causal Revolution has had a significant impact on various fields, including medicine, psychology, economics, and social sciences. In medicine, it has led to the development of evidence-based treatments and improved patient outcomes. In psychology, it has allowed for a better understanding of human behavior and the effectiveness of interventions. In economics, it has led to more accurate predictions and policies. In social sciences, it has helped to identify and address societal issues.

5. What are some challenges in studying the Causal Revolution?

One of the main challenges in studying the Causal Revolution is the complexity of causality and the difficulty in establishing causation in real-world situations. There can also be ethical concerns when conducting experiments to determine causality. Additionally, there is often a lack of data or limitations in data collection methods, which can make it challenging to establish causal relationships.

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