Why I Wrote this Book: Reflections and Expectations

I got my first hint of the dark world of causality during my junior year of high school.

My science teacher, Dr. Feuchtwanger, introduced us to the study of logic by discussing the 19th century finding that more people died from smallpox inoculations than from smallpox itself. Some people used this information to argue that inoculation was harmful when, in fact, the data proved the opposite, that inoculation was saving lives by eradicating smallpox.

"And here is where logic comes in," concluded Dr. Feuchtwanger, "To protect us from cause-effect fallacies of this sort." We were all enchanted by the marvels of logic, even though Dr. Feuchtwanger never actually showed us how logic protects us from such fallacies.

It doesn't, I realized years later as an artificial intelligence researcher. Neither logic, nor any branch of mathematics had developed adequate tools for managing problems, such as the smallpox inoculations, involving cause-effect relationships. Most of my colleagues even considered causal vocabulary to be dangerous, avoidable, ill-defined, and nonscientific. "Causality is endless controversy," one of them warned. The accepted style in scientific papers was to write "A implies B" even if one really meant "A causes B," or to state "A is related to B" if one was thinking "A affects B."

Clearly, such denial of causal thought could not last forever. The influence of artificial intelligence and the availability of powerful computer languages gave my generation the expectation that intuition should be expressed, not suppressed. And causality, it turns out, is not nearly as nasty as her reputation suggests. Once I got past a few mental blocks, I found causality to be smiling with clarity, bursting with new ideas and new possibilities. As the epilogue of my book summarizes:

             "Causality is not mystical or metaphysical.
             It can be understood in terms of simple processes,
             and it can be expressed in a friendly mathematical
             language, ready for computer analysis."

My intended audience includes: students of statistics who wonder why instructors are reluctant to discuss causality in class; students of epidemiology who wonder why simple concepts such as "confounding" are so terribly complex when expressed mathematically; students of economics and social science who often doubt the meaning of the parameters they estimate; and, naturally, students of artificial intelligence and cognitive science, who write programs and theories for knowledge discovery, causal explanations and causal speech.

I have aimed to provide each of these groups with separate ideas and techniques to make causal inference easier in their respective fields. The techniques will be a success only if they help resolve challenging problems in those fields, and I am fairly confident they will.

              Judea Pearl
             Los Angeles, California
             February 1, 2000