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.
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
It can be understood in terms of simple processes,
and it can be expressed in a friendly mathematical
language, ready for computer analysis."
Los Angeles, California
February 1, 2000