The Art and Science of Cause and Effect

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The Art and Science of Cause and Effect

• Adapted from a lecture by
• Judea Pearl
• available on his WEB site
• http//singapore.cs.ucla.edu/LECTURE/lecture_sec1.
  htm

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In the ancient world, causal agents were people
or animals or deities.
The serpent made me do it...
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• Even chance events were explained as messages
  from the Gods.
• The Book of Jonah tells that the sailors drew
  lots to determine who was responsible for their
  ordeal.
• When Jonah lost, he was thrown into the sea and
  had to live for 40 years in a whale.

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• The view of causation changed when people began
  constructing machines
• They had to figure out how the machines worked
  and how to correct problems
• Ascribing motivations to the machines did not help

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Galileo extended this model to the universe as a
whole
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Galileo used the strange new language of algebra
to describe movement in the physical world. d
t2
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Physicists discovered many useful laws that
explained things

• Snells Law
• Hookes Law
• Olms Law
• Joules Law
• all used equations to describe how the world
  actually worked

Hookes law (1678)
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Scottish philosopher David Hume argued that the
same principle could be applied to human affairs
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Causation and Correlation

• According to Hume, causation was a learnable
  habit of the mind
• When we see that two things go together we learn
  that one causes the other
• When the flame burns us, we learn to keep away
  from it

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But does the roosters crowing cause the sun to
rise?
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Bertrand Russell thought causation was a faulty
concept

• All philosophers imagine that causation is one
  of the fundamental axioms of science, yet oddly
  enough, in advanced science the word cause
  never occurs.
• The law of causality, I believe, is a relic of a
  bygone age, surviving, like the monarchy, only
  because it is erroneously thought to do no harm.

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Patrick Suppes thought causation was just a
shorthand for expressing complex relationships

• There is scarcely an issue of the Physical
  Review that does not contain at least one article
  using either cause or causality in its
  title.
• Science is full of abbreviations. We say
  density instead of the ratio of weight to
  volume. Why pick on causation?

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Russell is not convinced

• Causality is different.
• It could not possibly be an abbreviation,
  because the laws of physics are all symmetrical,
  going both ways while causal relations are
  uni-directional, going from cause to effect.
• Thus, we say that force causes acceleration, not
  the other way around, but the mathematical
  formula fma can work either way

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Karl Pearson agreed with Russell

• Pearson invented chi-square and the correlation
  coefficient
• He never talked about causation, only about
  correlation between variables.

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Pearson invented cross-tabulation and correlation
but never inferred causal relationships
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The randomized experiment offered the first
solution

• Sir Ronald Fisher invented the randomized
  experiment
• This is the only scientifically proven method of
  testing causal relations from data.

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Three Criteria of Causation

• Correlation Cause and effect must vary together
• Time Sequence The cause must come before the
  effect
• Non-Spuriousness The relationship between cause
  and effect cannot be explained by any third
  variable

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Experimental Proof

• Experiments establish time sequence by
  manipulating the independent variable
• Experiments establish non-spuriousness through
  random assignment to experimental and control
  groups
• Experiments establish correlation by measuring
  changes in the dependent variable in response to
  manipulation of the independent variable

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Engineers Use Causal Knowledge to Make things Work

• Their bottom line is pragmatic
• If they could make the sun rise earlier by
  getting the rooster to crow earlier, we might
  change our model of the universe

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If we blow up the lab, something was wrong with
our model.
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• In electronics, the causal models can be very
  complicated and useful
• A circuit diagram involves logic gates that
  are either on or off, 0 or 1
• They only work one way, from input to output

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Path Diagrams Can Be Used to Plot Causal Models
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An example of a multiple regression analysis
displayed as a pathdagram. By David Garson.
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Testing Causal Models is Difficult and Often
Controversial

• Experiments are the only guaranteed way to prove
  a causal model.
• But experiments cannot be done on many important
  social and criminal justice problems.
• So we approximate them as best we can with
  correlational data.
• It is important to check each step in this
  process carefully, paying close attention to time
  sequences and to controls for antecedent and
  intervening variables

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Equations Arent Enough

• Some social scientists just throw all their data
  into one massive regression equation, hoping that
  it will somehow control for all the variables.
• But this doesnt work, for a number of technical
  reasons.
• And because equations are not as good as diagrams
  for plotting time sequences

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The Bottom Line

• Causality is a difficult concept.
• But we cant do without it if we want to know how
  the world works.
• Or if we want to change the world.
• We have techniques that are helpful, but it is
  hard to get definitive answers because our
  ability to manipulate variables is often quite
  limited.

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Econometrics is a term for complex statistical
modeling done by economists. It is very complex
and mathematical but it doesnt work well on
sociological problems - see my paper on Myths of
Murder and Multiple Regression.
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If variations like unemployment, income
inequality, likelihood of apprehension and
willingness to use the death penalty are
accounted for, the death penalty shows a
significant deterring effect." Isaac Ehrlich,
New York Times, 2000
"All of the scientifically valid statistical
studiesthose that examine a period of years, and
control for national trendsconsistently show
that capital punishment is a substantial
deterrent." Senator Orrin Hatch, 2002
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"I do not think that regression can carry much of
the burden in a causal argument. Nor do
regression equations, by themselves, give much
help in controlling for confounding variables."
David Freedman
Regression on nationally aggregated data can
never yield reliable evidence on deterrence, pro
or con. The signal, if any, is hopelessly buried
in the noise. John Lamperti
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Just as Messrs. Lott and Mustard can, with one
model of the determinants of homicide, produce
statistical residuals suggesting that 'shall
issue' laws reduce homicide, we expect that a
determined econometrician can produce a treatment
of the same historical periods with different
models and opposite effects. Econometric
modeling is a double-edged sword in its capacity
to facilitate statistical findings to warm the
hearts of true believers of any stripe.
Franklin Zimring and Gordon Hawkins, Concealed
Handguns The Counterfeit Deterrent, The
Responsive Community 7 46-60, 1997
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The Bell Curve by Herrnstein and Murray put
American social scientists in an uncomfortable
place. The conclusions of the book are
unwelcome, while the methods of the book appear
to be the standbys of everyday social science.
The unstated problem for many commentators is how
to reject the particular conclusions of The Bell
Curve without also rejecting the larger
enterprises of statistical social science,
psychometrics, and social psychology.
Clark Glymour in Intelligence, Genes and
Success Scientists Respond to the Bell Curve
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there is much uncertainty as to the correct
empirical model that should be used to draw
inferences, and each researcher typically tries
dozens, perhaps hundreds, of specifications
before selecting one or a few to report. Usually,
and understandably the ones selected for
publication are those that make the strongest
case for the researchers prior hypothesis. The
data analyzed are not sufficiently strong to lead
researchers with different prior beliefs to reach
a consensus regarding the deterrent effects of
capital punishment. Right-winger,
rational-maximizer, and eye-for-an-eye
researchers will infer that punishment deters
would-be murderers, but bleeding-heart and
crime-of-passion researchers will infer that
there is no significant deterrent effect.
Walter McManus, Journal of Political Economy, 1985
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In many quantitative disciplines, most typically
econometrics, the appropriate method is to assume
a statistical model, then collect the data, then
test the model by comparing the statistics with
the model. If the model does not fit it is
rejected. This is supposedly "sticking out one's
neck," which is presumably the macho Popper
things to do. There are various things
problematic with this prescription if you follow
the prescription, and your data are any good,
your head gets chopped off people know their
head will get chopped off, nobody follows the
prescription. They collect data, look at their
data, modify their model, look again, stick out
their neck a tiny bit, modify the model again,
and finally look around with a proud look on
their face and a non-rejected model in their
hand, pretending to have followed the Popperian
prescription. Thus the prescription leads to
fraud.
Jam de Leeuw in Trends and Perspectives in
Empirical Social Research
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