1. History of Causal Analysis
Farrokh Alemi, Ph.D.
This is an introduction to the course on comparative effectiveness, HAP This presentation is based on April 2013 lecture by Judea Pearl titled "The Mathematics of Causal Inference: With Reflections on Machine Learning. In that lecture Pearl traces history of causal thinking. This lecture is part of course on comparative effectiveness at George Mason University. This course introduces non-statisticians to causal analysis. It is an applied course so students learn causal analysis without delving into mathematical proofs of various theorems.

2. One who knows and knows he knows; he is learned, follow him!
One who knows and knows not that he knows; he is asleep, wake him!
One who knows not and knows he knows not; he seeks to learn, teach him!
One who knows not and knows not that he knows not; he is a fool, shun him!
Old Persian Poem Also attributed to Khalil ibn Ahmad al-Faraheedi
I want to start with an old Persian poem. The poem says that a person who knows and is aware that he knows; is a wise person. We should follow him. One who knows that he is not aware of his own knowledge; he is in essence asleep, so wake him! People who know that they do not know a topic, well teach them. Even if they are not sure what they know, they at least have recognized the need to learn. One who knows not and knows not that he knows not; he is a fool. There is no hope for him!

3. Imaginary Causes
At least 4 phases can be recognized in the history of causal analysis. The first era is when humanity was not even aware that events occur because of a reason. This area is best described as magical time. Examples include Greek mythology where events occur because of various gods and not because of anything within our grasp. Storms and earthquakes were controlled by angry gods and not by causes that could be tracked and understood or even controlled.

4. Imaginary Causes
A good example of how humans used to think about causality is given by the biblical retelling of Adam and Eve story. Adam wants to know why things happen and he eats the fruit of the tree of knowledge. In essence, Adam had an urge to ask WHY and the capacity to find causal explanations for events. When god asks Adam why did you eat the apple. He reasons: "The woman whom you gave to be with me, She handed me the fruit from the tree; and I ate." Eve is just as skillful: "The serpent deceived me, and I ate.“ Both Adam and Eve are giving possible causes of their behavior.

5. There is a Reason Why Events Occur
Eureka
Then comes a period in which engineers and scientists see functional reasons behind events. For example, Archimedes describes a pump that can be used to move water up the hill. Galileo laid out the principles of first describing an event and then asking why it has occurred. He went as far as using mathematics to describe the event. He led the effort that scientists should look for causes of events. This way of thinking led to his eventual death, when he was hanged for not thinking that god was the cause of events. It also led in the subsequent century to a flood of new scientific findings. Examples are Snell law, Hookes law, Ohm's law, and Joule's law. These scientist described their observations and identified causes for them. They created fundamental principles of physics.

6. Describe how it happens
How & Why
Describe how it happens
David Hume was the first person to see the role of mechanism in establishing causes. He goes as far as pointing out that if we describe a mechanism, if we say how things happen, then we will know why it happens. He goes further and claims that one can actually can observe the effect of cause on effect. He gives the example of flames causing heat. We can see and feel that where there is a flame we can feel the heat. In addition, Hume knew that barometer reading occurs in constant conjunction to the rain, but does not cause the rain. But he does not clarify this issue.

7. Purging Causality from Science
“…The law of causality, I believe, is a relic of bygone age, surviving only because it is erroneously supposed to do no harm ..."
The
Lord Russell

8. Purging Causality from Science
“… the laws of physics are all symmetrical, going both ways, while causal relations are unidirectional, going from cause to effect ..."
Russell argues further that causality is ill suited for science because in nature we just see association and not causality. Causality is unidirectional and association is bidirectional. In his view, events in nature are bidirectional. Thus speed of falling is associated with distance an object has fallen and vice versa. There is no causation here just an association. Engineers and physicists who were focused on predicting functional events ignored him but statisticians did not.
Lord Russell

9. Purging Causality from Science
“… that co- relation must be the consequence of the variations … partly due to common causes ..."
Galton discovered correlation in He argued that two variables may co-vary because they are affected by a common cause and that this co-variation can be measured through co-relations without attention to the common cause of the events.
Francis Galton

10. Purging Causality from Science
“Contingency and correlation - the insufficiency of causation"
Pearson, the father of modern statistics, was adamant that no one should talk about causes. We could only know correlation or association and that is all that can be discovered. His work led to the phrase that “correlation is not causation.” Pearson led a crusade against study of causation and succeeded to exterminate the notion of causation from statistics. Even to this date, some statisticians believe that causation cannot be studied.
Karl Pearson

11. Disguised Causal Claims
Of course, even though statisticians did not use the word causality, the studies they were analyzing were making causal claims and supporting these claims with strength of associations. Policymakers still interpreted the studies as causal even though the statisticians did not mention the word causality in their report. Engineers wanted to see how things worked and would use statistics but interpret it as if it implied causality. Physicists break through atoms and the man was sent to the moon all without saying any of the science it was based on was causal. Cures were found and patients’ lives were saved without calling the underlying studies as causal.
Karl Pearson

12. Disguised Causal Claims
A false truce was made. Like don’t tell and don’t ask principles, statisticians agreed not to mention causality and policymakers agreed to act as if that is what they were reporting. Papers which were clearly causal had to remove the word causality and imply it.
Karl Pearson

13. Revolt Starts Structural Modeling Economists
Of course, the situation was intolerable. Scientist need to talk about cause and effect. So outside statistics a revolt started.

14. Revolt Starts Propensity Scoring Sociologists Karl Pearson
Sociologists like Rubin started talking about removing confounding using propensity scoring.
Karl Pearson

15. Engineers Function Revolt Starts
Engineers started saying we do not care about these debates just give me something that works. They focused on causes of events and manipulation of these causes.

16. Probabilistic Contrast theory
Revolt Starts
Psychologists
Probabilistic Contrast theory
Psychologist started describing human thinking in causal terms.
Karl Pearson

17. Artificial Intelligence
Revolt Starts
Artificial Intelligence
Causal Models
AI scientists abandoned efforts to make smart machines without notions of causality and incorporated causal thinking in their efforts.

18. Terminology Exposion
Each discipline uses its own words to describe the same concepts. Multiple approaches are worked out each with different terminologies.

19. A Uniform Approach
Pearl provides a comprehensive model of causality and publishes his book in this area in Defines causality as a manipulative concept. He argues, like most engineers before him, a cause can be distinguished from probability and association statements. He focuses on manipulation of causes. One has to show that when the cause is present the effect occurs and when absent it does not. The methods and models he proposes provide a uniform approach across the sciences.

20. Return of Causality to Statistics
Because Pearl provides clear methods for study of causality, statisticians return to the issue. An examination of articles in statistical journal shows a growing use of the word cause and effect within statistical literature.

21. Applied Causality
This course is a hands on application of various concepts of causal analysis. You may not learn the theoretical issues in depth or prove the mathematical theorems but you are expected to use causal analysis to make sense from your data. But a little history could help put this applied course in context. These slides were intended to provide you with a brief historical understanding of the issues