Introduction
to Causal Arguments
One of the most important uses for inductive reasoning
is to argue causation. Consider the following example:
A bicyclist
moves into the traffic lane in order to pass a truck illegally parked in the bike lane. The driver of a car approaching from
the rear slams on her brakes in order to avoid hitting the bicycle. A following car fails to stop in time, and smashes into
the back of the first. The insurance companies disagree about who should be held responsible, and they go to court to decide
who caused the accident.
What arguments are likely to be made in court? The
bicyclist's lawyer will probably claim that the illegally parked truck caused her client to swerve into the lane of
traffic. The lawyer for the driver of the first car will probably claim that the bicyclist's actions caused her client
to slam on the brakes. The lawyer for the second driver will probably claim that the first car's sudden stop caused
his client to smash into its back.
None of these claims seems to fit the pattern of
an inductive argument, because none of them seems based on observation or experience. But, in fact, they do fit that pattern.
The bicyclist's lawyer, for example, is actually arguing that:
- Normally the bicyclist would
have continued in the bike lane, but in this instance he swerved into the lane of traffic.
- The only signficant difference
between "normally" and "in this case" is the presence of the illegally parked truck.
- Therefore, the truck caused
the bicyclist to swerve.
The lawyers for the drivers are making similar arguments:
the first, that the only signficant difference was the swerving bicycle; and the second, that the only signficant difference
was the suddenly braking car. Like inductive reasoning, then, these causal arguments are based on observed instances. (In
this case, no observations are needed to convince us that the bicyclist would not normally have swerved or the first driver
would not normally have braked suddenly. But if, for some reason, observations were necessary, we could design a study of
automobile and bicycle traffic on that street, or survey drivers and bicyclists about their experiences, or in other ways
provide evidence to verify the part of the premise describing the normal pattern of traffic. For more on this, see the section
on Studies, Surveys, Polls, and Experiments.
These causal arguments, then, follow the form of
an inductive argument with one important exception: whereas an inductive argument carries as part of its second premise the
implication that there is otherwise no significant difference, these causal arguments carry the implication that there
is only one significant difference: for the bicyclist, the truck; for the first driver, the bicycle; for the second driver,
the first car.
How can we know that there is really only one
significant difference? In real-life situations, we cannot usually be certain of that, since the world in which we live is
a very complicated and intricate place. If, however, there is a strong likelihood of causation and there are no other apparent
causes in evidence, then the argument will seem convincing. Two rules to remember in dealing with causation are:
- The cause must precede the
event in time. On one hand, arguments that have the effect before the cause are examples of the relatively rare fallacy of
reverse causation. One the other, arguments whose only proof of causation is that the effect followed the cause
are examples of fallacious post hoc reasoning.
- Even a strong correlation
is insufficient to prove causation. Other possible explanations for such a strong correlation include coincidence,
reversed causation, and missing something that is the cause of both the original "cause" and and its purported "effect."
In the
trial, for example, the second driver's lawyer could not argue that his client hit the first car because the
first car stopped suddenly if reverse causation, post hoc reasoning, or a common cause is found. An example of reverse causation
would be that the accident occurred before the first car began to stop, and it only came to a sudden stop because
it was hit. An example of post hoc reasoning would be that the only connection between the stopping and the accident is that
the car stopped first (say, Tuesday), and the accident happened later (say, Thursday). An example of a common cause would
be that the reason the first car stopped suddenly and the reason that the second car hit it are the same: they were
both side-swiped by a large recreational vehicle.
But what if, as the trial progresses, evidence is
introduced to show that the bicyclist has a history of causing accidents by swerving in front of cars, or that the first driver
has been involved in several accidents in which she caused her brakes to lock, or that the second driver was speeding and
the street was slick? Such information could lead to very different causal arguments, where the implied claim does not concern
"the only signficant difference," but rather "the only significant commonality." The bicyclist's lawyer might argue, for example,
that her client's swerving may have caused the first driver to brake suddenly, but that the accident was caused by
the way in which the first driver hit the brakes, and that she has caused similar accidents that way in the past. The first
driver's lawyer, on the other hand, might argue that the accident was caused not by her client's braking, but by the
second driver's unsafe speed, and the slick condition of the road, and that these factors have often caused other accidents.
Both lawyers would be using a causal argument based on "the only signficant commonality."
Often, complex causal arguments use a mixture of
"difference" and "commonality" reasoning, to show that the proposed cause is the only significant difference in cases where
the effect did not occur, and the only significant commonality in cases where the effect did occur.
The strength of a causal argument, then, relies on
three factors:
- how acceptable or demonstrable
the implied comparison is (for example, do we think that there
is a basic similarlity in most respects between the circumstances of this accident and those of the many other times bicycles
and cars have traveled on this street safely;
- how likely the case for
causation seems to be (for example, do we think that a bicycle
swerving into an car's lane can cause an accident?);
- how credible the "only
significant difference" or "only significant commonality" claim is
(for example, do we believe that the illegally parked truck is the only significant difference between this case and the many
other times bicycles and cars went down that street without an accident?).
