PHIL 5973: Mental Causation Seminar
University of Arkansas, Fall 2003
Topic: Causation Background
This course covers the topic of mental causation. While we will have many readings dealing
with mentality, we will have none dealing with causation in a general sense. However, many of
the authors we will read explicitly rely on one of the standard analyses of causation (e.g.,
Davidson, Fodor, and Kim accept a “nomological” analysis; Lewis, LePore, Loewer, and
Yablo a “counterfactual” analysis). The main goal of this opening class is to present, at a
level of great generality, the most popular approaches to analyzing the concept of causation,
and raise general worries for each of them. (I stress, this is a very brief and incomplete
presentation of the topic.) An analysis of causation will fill in the blank of the following
claim: a caused b if, and only if, _______.
1. Humean, regularity, or “nomological” analyses
The general form of analysis on these views is: a caused b if, and only if, i) a and b occurred,
ii) a falls under type j and b falls under type k ,and iii) it is a law that k-type events follow jtype events.
For example: The executioner’s injection of drug X into the convict’s arm caused him to
die. Why? ‘The executioner’s injection of drug X into the convict’s arm’ falls under the type
‘poisoning by drug X’ and the convict’s death falls under the type ‘death’, and it is a law that
deaths follow drug X poisonings.
*Note:
a) In this analysis, iii) informs us that causes precede their effects. In those to
follow, no such requirement is given specifying that a must precede b.
b) ‘a’ and ‘b’ stand for particulars—event or property tokens. We should be careful
to distinguish singular causal judgments from general (type-level) causal
judgments. The Humean explains the former in terms of the latter.
Objections:
a) What is a law? And is this understanding of a law appropriate for backing causation?
A developed Humean analysis of causation will have to provide standards for lawhood. One
standard might be: It is a law that k-type events follow j-type events if, and only if, all
observed j-type events have been followed by k-type events. But, one might object, this
standard is both too weak and too strong. It might be too weak in that not enough
observations have been made, the correlation could be coincidental, or k-type events might
be the result of a common cause, etc. The standard might be too strong in that there seem
to be laws with exceptions that nevertheless back causal claims. These are ceteris paribus laws.
Most, if not all, psychological “laws” are of this type, but many (Fodor, for one) think such
laws are sufficient to back causal claims.
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Coming up with appropriate standards for lawhood is tough, and Humean analyses of
causation might just be moving the bulge in the carpet to this area.
b) This analysis doesn’t distinguish real causation from epiphenomenal causation, or cases
of common causes.
There are some cases in which, though k-type events reliably follow j-type events, our
intuitions are that j-type events do not (or at least might not) cause k-type events. In part,
this may be because some i-type event is responsible for both the j-type and k-type events. If
the i-event is simultaneous with the j-event, we have a case of epiphenomenal causation. If
the i-event precedes the j-event, we have a “common cause” case.
Examples:
Epiphenomenal Causation: My dog has been trained so that it reliably sits whenever
I issue the command “Sit!”. There is a law-like connection between my uttering
words that mean sit and her sitting behavior, and there is no such connection with
words with different meanings (e.g., ‘come’, ‘stay’, etc.). But, one might think that
the meaning of the word ‘sit’ nevertheless is causally irrelevant to her behavior. My
dog doesn’t sit because of the meaning of the word ‘sit’, but because of the mere
sound of the word and its association with rewards. (Maybe dogs do understand
content, though. Then we should give an example for a “dumber” animal.)
Common Causes: It has been observed that a certain symptom (say, a peculiar type
of rash) is always followed by illness X. Because of this law-like connection, one
concludes that the rash causes illness X. Actually, this is mistaken. What happens is
a virus is present, which causes two different effects: the rash and the illness.
Because the rash occurs before the illness, there is the appearance that the rash
causes the illness. In fact, there is no causal connection between the rash itself and
the illness (e.g., one could acquire the rash, and that would not cause one to then
contract the illness).
c) There seem to be cases of real causation, absent regularities.
Example: Shaq breaks his foot in April, and the Lakers lose him for the playoffs. The
Lakers end up losing in the second-round. It is plausible that Shaq’s broken foot caused the
Laker’s to lose so early in the playoffs. However, there is no regularity between Shaq’s
broken feet and early playoff exits.
d) Humean analyses emphasize the sufficiency aspect of causation, but causes must also be
necessary (in the circumstances) for their effects. Plus, causes needn’t be strictly sufficient for
their effects.
Causes needn’t be strictly sufficient, because it certainly seems true that match-strikings can
cause fires (though they’re not strictly sufficient for fires).
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Conversely, something might be strictly sufficient for an outcome, though not cause it.
Maybe the initial conditions of the Big Bang were sufficient for our being in this classroom
today. But those precise details might be at the wrong level of abstraction to explain the
macro-phenomena of our being here-now.
*Because of these kinds of worries, John Mackie famously introduced his INUS conditions
for causation.
Humeans of interest (for the purposes of this class): David Hume, Donald Davidson, Jerry
Fodor, and Jaegwon Kim
2. Probabilistic causation
The general form of analysis on these views is: a caused b if, and only if, i) a and b occurred
and ii) a’s occurrence raised the probability that b (as compared to a’s non-occurrence).
*This form of analysis can be given a more technical, mathematical formulation in terms of
conditional probability. Typically, many other bells and whistles are added in an attempt to
avoid some of the objections considered below.
One main motivation for this view is that it captures statistical generalizations that back
many causal claims. For example, lung cancer doesn’t invariably follow from a life of
cigarette smoking. But there is a statistical correlation strong enough to back the claim that
cigarette smoking causes lung cancer. The probabilistic analysis captures this judgment, and
it is not clear that the standard Humean view captures it as well (though developments in
this tradition—e.g., by J.S. Mill, Mackie, etc.—offer substantial improvements). Obviously, a
probabilistic analysis is also better suited at capturing indeterministic causal relations. This is a
point in favor of probabilistic analyses if our world is, in fact, indeterministic (as quantum
mechanics suggests).
Objections:
a) Probability raising is symmetric, but causation is asymmetric. This analysis does not
distinguish cause from effect.
b) This analysis succumbs to the same worry about epiphenomenalism that besets the
Humean version.
c) Sometimes causes actually decrease the likelihood of their effects.
Example: Katie doesn’t want to study for her philosophy exam, so she goes out to the bar
instead. While drinking at the bar, she runs into someone who took the same exam last year.
Katie makes arrangements to get a copy of this person’s exam and memorizes the answers
just before her exam. Katie receives an ‘A.’
It seems that Katie’s going to the bar and not studying that night was a cause of her earning
an ‘A’ on the philosophy exam. But it certainly doesn’t seem that going to the bar and not
studying for an exam increases the likelihood of receiving an ‘A.’
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*It should be obvious that depending upon how the causal-candidate is described, the
probability judgments will differ. E.g., ‘going to the bar and running into someone who took
the exam the year before’ might increase the probability of earning an ‘A’.
d) Conversely, sometimes events increase the likelihood of an outcome without causing it.
Example: Katie stays at home and studies for her philosophy exam to the best of her ability.
Unfortunately, she focused on the wrong material. During the exam, Katie panics—the
material is not at all what she expected. Out of desperation, she shamelessly cheats off of
the star pupil beside her. Katie receives an ‘A’.
Though studying for the philosophy exam to the best of her ability does increase the chances
of Katie receiving an ‘A’, it seems clear that this studying did not cause her to receive an ‘A’.
Instead, her cheating caused her to receive an ‘A’.
e) Because of these worries, one might think that probabilistic analyses are appropriate for
general (type-level) causal claims, but not for singular causal claims.
Probability theorists of interest: Hans Reichenbach, Christopher Hitchcock, Ellery Eells,
Patrick Suppes, and Nancy Cartwright
3. Counterfactual analyses
The general form of analysis on these views is: a caused b if, and only if, i) a and b occurred
and ii) b would not have occurred if a had not occurred.
Objections:
a) How are the counterfactuals evaluated?
A common worry about counterfactuals concerns their capacity for being evaluated as true
or false. What kind of “fact” is a true counterfactual statement? How can we know how the
world would have been had such-and-such, when such-and-such didn’t occur? We can easily
see why Empiricists have traditionally been uneasy with counterfactual statements, since no
examination of the world can verify them directly.
Still, in everyday speech we accept counterfactual statements frequently (or at least argue
about them in a way that takes them as truth-evaluable). E.g., Bobby Kennedy would have
won the Democratic Party nomination in 1968, had he not been assassinated. The U.S.
Olympic basketball team wouldn’t have lost to the U.S.S.R. in 1972 had NBA (and ABA!)
players been allowed to play.
Starting in the 1960’s, however, David Lewis developed a possible-worlds theory for
evaluating the truth of counterfactuals. In order to determine if b would have occurred had
not-a, we must “look” to the “nearest possible world” in which not-a and “see” whether b.
There are obvious difficulties with this proposal. Of course, we cannot observe these worlds
in any traditional sense (e.g., with our 5 senses). And how do we know what counts as the
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“nearest possible world”? Two factors must be balanced in making such determinations:
qualitative resemblance and fidelity to the laws of the actual world.
b) Cases of causal pre-emption and overdetermination are counter-examples to a simple
counterfactual analysis.
Pre-emption example: One way or another, John is going to be told the bad news.
His two friends, Jack and James, know that one of them has to tell John. Jack breaks
the news to John, and John breaks down and cries. But if Jack hadn’t told John the
news, James would have.
It seems clear that ‘Jack telling John the bad news’ caused ‘John’s breakdown.’ But,
this does not follow from a simple counterfactual analysis of causation. For, in the
“nearest possible world” in which Jack doesn’t tell John the bad news, James tells the
news instead. In such a case, John still breaks down and cries.
Overdetermination example: A convict before a firing squad is struck in the heart,
simultaneously, by the bullets of two sharpshooters. The convict dies. Did either, or
both, of the sharpshooters kill (i.e., cause the death of) the convict? The convict’s
death, it seems, does not counterfactually depend on either pulling the
trigger/shooting the convict. For example, in the “nearest possible world” in which
shooter A either doesn’t pull the trigger or fails to hit his target, the convict still dies
because shooter B performs that task (as B does in the actual world). So, A’s pulling
the trigger/hitting his target didn’t cause the convict’s death. For parallel reasons,
B’s pulling the trigger/hitting his target didn’t cause the convict’s death either. But,
didn’t they each cause his death? (I.e., Wasn’t his death overdetermined?)
One available response is to say that they jointly caused this death, and the effect
would have been different had only one of them struck their target. But, doesn’t it
sometimes happen that the effect is the very same regardless if one or two possible
causes run their course?
Counterfactual theorists of interest (for the purposes of this class): David Lewis, Ernie
LePore, Barry Loewer, and Stephen Yablo
4. Primitivism
According to the Primitivist, causation is unanalyzable. There are certain concepts that are
fundamental and are not to be analyzed in more basic terms.
Objection:
a) This just gives up the game. We could always go primitivist about any concept, but we
should try to find an analysis if possible. And the connections between causation and both
laws and counterfactuals seem especially promising routes to explore.
Primitivists of interest: G.E.M. Anscombe, David Armstrong, and Michael Tooley
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