Copyright © 2003 jsd
First, let me explain why it is important to think clearly about cause-and-effect relationships.
It turns out that judgments about cause-and-effect often lead to real-world actions. For example, you might find that certain locations are associated with a high number of weird diseases. If you think there is something about the location (e.g. Picher, Oklahoma) that is causing weird diseases, you might well decide to take drastic action, such as moving everybody out. On the other hand, it may turn out that the location (e.g. the Mayo Clinic) is not causing the diseases, and it would be a really bad idea to move everybody out.
These are not made-up examples: Anybody in ZIP code 74360 (Picher) would be better off almost anywhere else, while anybody in ZIP code 55905 (Mayo Clinic) would be worse off almost anywhere else.
There are innumerable other examples where one must determine whether there is a causal relationship between two things. Large numbers of lives and dollars depend on such determinations every day.
In some circles it is fashionable to think that science teachers are much much more logical than politicians. Yet every day we expect elected officials to make determinations such as
It would be nice if science teachers would set a good example, and teach others how to think clearly about cause-and-effect.
The notion of cause-and-effect is more subtle than the notions of ordinary logic, as we now discuss:
The following three ideas are equivalent:
And they are closely related to (i.e. a limiting case of):
But as we shall see, those four ideas are not anywhere close to
To see this, consider the following example:
and I have a dataset with many rows (one for each person) and two columns (A and B). Within this dataset we observe that A ⇒ B. You can check that in every row, we either have B or ∼ A; that is, either the person was bitten, or the person did not show rabies symptoms.
On the other hand, it would be absurd to suppose that A causes B. The person’s rabies symptoms are not the cause of the bite. The cause cannot happen after the effect.
We conclude that in general, it is not safe to infer a cause-and-effect relationship from an ordinary collection of data. You can infer that A implies B, but not that A causes B.
Even if you have timing information as well as statistical information
you cannot safely infer that the rooster caused the dawn.
Correlation does not imply causation. Implication does not imply causation. The pitfalls of not properly making such distinctions are discussed in Feynman’s sermon about cargo-cult science. The most widely-available source for this is reference 2.
Now that we have seen some of the pitfalls, let us move into positive territory and see what we can properly say about causation.
This is not easy. Experts have struggled for centuries, trying to find an exact definition of causality. The best modern work on this topic has its roots in epidemiology, and borrows some colorful terminology from that field. For instance, the term “intervention” corresponds essentially to what a physicist would call a “perturbation”. Meanwhile the term “counterfactual” corresponds essentially to what a physicist would call “hypothetical”.
For some good modern work in this area, rich in technical details, see reference 3. A nice “data driven” approach can be seen in reference 4, especially reference 5.
To a first approximation, in simple cases we can say the following: A statement about causation is a predictive hypothesis: it says you cannot remove the effect without removing the cause. To say the same thing the other way: given any intervention that allows the cause to remain, the effect will remain.
Here we begin to see the difference between a statement about statistics and a statement about causation.
Like most other scientific hypotheses, such statements about causation cannot be proved, only disproved. If we strangle the rooster and the sun comes up anyway, we have disproved the hypothesis that cock-crowing causes the dawn.
If a hypothesis is repeatedly tested and not disproved, we gradually raise our estimate of the reliability of the hypothesis. It is possible (under certain assumptions) to quantify the reliability of such hypotheses, but the details are beyond the scope of this discussion.
As another example, suppose we intervene to reduce the number of people who get bitten, perhaps by teaching people to stay away from wild animals. That will reduce the incidence of human rabies symptoms, OTBE1 (Other Things Being Equal). This is consistent with all of our hypotheses; it doesn’t disprove them. Remember, causation is disproved if we can remove the alleged effect without removing the alleged cause, and that didn’t happen in this scenario. Both variables declined together, so nothing is disproved.
Most cause-and-effect relationships involve multiple factors. Sometimes there are factors in series: You get rabies symptoms if you get bitten and the animal was infected and you don’t get treatment. Sometimes there are factors in parallel: you can cause a piece of paper to separate into two pieces by ripping it or cutting it with a knife or cutting it with scissors.
We must be careful when testing causation hypotheses if there are multiple factors involved, as we see from the following scenario: Suppose we intervene by vaccinating a bunch of people, and then take some more data. We find a reduction in the number of people with rabies symptoms, without any reduction the number of people bitten by wild animals. This disproves the hypothesis that getting bitten is the sole causative factor ... but we already know that non-immunity is not the sole causative factor, so we must formulate a new hypothesis involving multiple factors in series.
Things get very tricky if there are are multiple low-probability factors in series, or if there are multiple high-probability factors in parallel.
Note 1: Of course all the rabies hypotheses considered here are oversimplified – but they are not wrong and could be used as the basis of non-ridiculous (albeit imperfect) policy decisions.
Note 2: We can do a pretty good job of evaluating the various hypotheses without knowing anything about the temporal ordering of the bites and the rabies symptoms. On the other hand, if we do have timing information we can use it to bolster the conclusion.
These examples illustrate the general rule that to infer a cause-and-effect relationship, you need to study how the system responds to interventions (i.e. perturbations).
You need to be careful to specify the details of the intervention.
You also need to be careful to specify what OTBE means. As an illustration of this pitfall, consider a lever with four labeled points:
and we want to know whether point Y rises when I raise point W. Well, that rather depends on whether the fulcrum is at point X or point Z! To repeat: it does not suffice to say that the intervention is raising W. Raising W at constant X is very different from raising W at constant Z.
Before we go much farther, we ought to say explicitly what we mean by “equality”.
In the fields of mathematics and formal logic, there exists the concept of an equivalence relation. A relation X(⋯,⋯) between two items is an equivalence relation if it is reflexive, symmetric, and transitive:
The familiar and obvious example of such a relationship is the usual “equality” operator:
Note that an equivalence relationship need not – and usually does not – depend on every aspect of the items. For example, if we have a red triangle, a green triangle, a blue triangle, and a blue square, as shown in figure 2, the two triangles are equivalent with respect to number of vertices, while the two blue items are equivalent with respect to color.
Given an equivalence relationship, we can define the idea of equivalence class. A give set is an equivalence class if every pair of members of the class satisfy a given equivalence relationship. For example, if the equivalence relationship is “same number of vertices”, all triangles are members of one equivalence class while all quadrilaterals are members of a different equivalence class.
As an instructive example of an operator that does not express an equivalence relationship, consider the notion of proximity. It is reflexive (because A is always near A) and symmetric (because if A is near B, B is near A), but it is not transitive. There are plenty of cases where A is near B and B is near C, but A is not near C. This give rise to the proverb:
Beware of the following contrast:
In non-scientific vernacular language and thought, there is a
close connection between “force” and “cause”, as we see from
the similarity of meaning in the following sentences:
|When it comes to physics, it is a horrible mistake to confuse force with cause. Anything that blurs the distinction between force and cause is a big step in the wrong direction.|
|Similar remarks could be made about numerous other words, including “pressured”, “impelled”, et cetera.||In particular, as we shall see in section 9, it is a mistake to think that the equation F=ma means F causes ma (unless you stretch the terminology to say that ma causes F).|
|Given this conflict between vernacular terminology and technical terminology, it is unsurprising that non-experts are often confused about the distinction between force and cause.|
In some informal sense, every element in a feedback loop is both a cause and an effect. This applies to negative feedback as well as positive feedback. The naïve assumption that causes must be distinct from effects is untenable.
Informally, this does not violate the requirement that cause should precede effect, because if you disturb any given element of the feedback loop, the effects ripple around the loop over time, affecting other elements, eventually affecting the given element itself.
Consider the RS latch shown in figure 3. Normally the R and S inputs are at a “high” logic level. A low-going pulse will force output B high. Normally output A will go low, and the system will latch up in that state. Conversely, a low-going pulse on input S will force output A high. Normally output B will go low, and the system will latch up in that state.
Hint: S stands for “set” and R stands for “reset”.
When the system is latched up, it is impossible to identify (or even define) a “cause” or an “effect” in such a system, in terms of conventional, formal ideas. Testing to see what happens if we remove the cause doesn’t work, since the cause – namely a low-going input pulse – has long since been removed.
(If we get to watch a sufficiently rich history of such a system, we may be able to identify the causative factors.)
The S-latch shown in figure 4 is even nastier. A low-going pulse on the S input will cause the system to lock up in the state where A is high and B is low. There is simply no way to unlock it.
Here’s another example: Figure 5 shows a simplified version of a feedback loop that shows up in educational psychology. Other feedback loops are very common in technology; for example, the thermostat on a household heating/cooling system is part of a feedback loop. There are also feedback loops involved in climate change, including some negative feedback paths along with some very scary positive feedback paths.
In the figure, red and black are used to show the elements of the feedback loop, and the interactions between them. Gray is used to show external causative factors, external to the loop.
The external causative factors are just causes, not effects. In contrast, each element internal to the loop is both a cause and an effect. Because this is a positive feedback loop, once it gets going it keeps going, even if you remove all of the external causes. At this point, examining the loop will not tell you what the original external cause(s) might have been. The present situation is controlled by internal causes, internal to the loop.
What’s worse, at this level of detail, figure 5 is just as irreversible as figure 4. The figure does not show any way to unlatch the feedback loop, once it gets latched up. One hopes that there are other factors, not shown in the diagram, that will allow the real-life situation to be unlocked.
From a policy and planning point of view, you would very much like to know what the original causative factors were, so you can take action to prevent similar feedback loops from starting up in the future. However, you can’t figure that out by looking at the present state of this loop. Furthermore, knowing the original cause(s) doesn’t necessarily tell you how to change the state of this loop as it stands.
Also in figure 5 are symptoms, shown in light blue. These are external effects. They are not causes, and not part of the feedback loop.
Applying some intervention to scrub away the symptom does not change the cause-and-effect relationships within the loop. For example, suppose you find some observable quantity that is initially a 100% reliable symptom, initially 100% correlated with some problem you care about. If you scrub away the symptom, it does not cure the problem. All it does is break the correlation, making the quantity no longer a reliable symptom.
We can deepen our understanding of this with the help of figure 6.
In this circuit, there are three different inputs that could set the latch, plus two inputs that could reset the latch. Suppose it is latched in the A state and but we desire to reset it to the B state. We proceed as follows:
Normally, when there are no S or R factors acting on the latch, you cannot determine by looking at the present state of the latch which of the inputs “caused” the current situation. If the latch is set, you don’t know which of the three possible inputs set it. If the latch is reset, you don’t know which of the two possible inputs reset it. Either way, the cause is unknowable, lost to history.
Much of what was said about the latch circuits applies to the psychological situation in figure 5. In particular, it is entirely possible to have an undesirable positive feedback loop that you can get into but cannot get out of. Also, very often it is utterly impossible to ascertain what caused the current state just by looking at the current state.
It has been known for some time that the basic laws of mechanics do not express causation. Galileo made a point of this in 1638:
The present does not seem to me to be an opportune time to enter into the investigation of the cause of the acceleration of natural motion, concerning which various philosophers have produced various opinions .... Such fantasies, and others like them, would have to be examined and resolved, with little gain. For the present, it suffices .... to say that in equal times, equal additions of speed are made.
– reference 6, page 203 of the National Edition.
The laws of physics must say what happens, but they need not say how it happens, and they almost never say why it happens.
This is tremendously important. In the footnote on page 159 of reference 7, Stillman Drake observes:
Rejection of causal inquiries was Galileo’s most revolutionary proposal in physics, inasmuch as the traditional goal of that science was the determination of causes.
Drake discusses that point at more length in his Introduction (reference 7):
What was lacking in physics, from the time that Aristotle coined that word to name the science of nature, was the idea that actual measurement could contribute anything of real value to any science. The object of science, as set by Aristotle, was to find out the hidden causes of events in nature. Measurement could not reveal underlying causes of the kind required by philosophers, so measurement had no place in physics.
Let me say it again: Galileo’s statement is the epoch, i.e. the defining moment, defining physics as we know it. Modern physics starts from the realization that we don’t need to know whether F causes ma, or vice versa, or both, or neither; for a wide range of purposes it suffices to know that F equals ma.
This is what sets modern science apart from medieval science. This is what sets physics apart from metaphysics and philosophy.
Newton went to school on Galileo (literally and figuratively). Seventy five years later, Newton expressed the same idea, explicitly disclaiming causation:
Hactenus Phaenomena caelorum et maris nostri per Vim gravitatis exposui, sed causam Gravitatis nondum assignavi.... et Hypotheses non fingo.
Hitherto we have explained the phenomena of the heavens and of our sea by the force of gravity, but have not yet assigned the cause of this force.... and I will not pretend to guess.
There is a time and a place to deal with questions of causation, but introductory mechanics definitely isn’t it. Anything that blurs the distinction between causation and force sets science back almost 400 years.
Let us consider Hooke’s law: F=−kx.
Because equality is symmetric (as discussed in section 4), the equation F=−kx as exactly the same meaning as −kx=F.
There are many ways of applying Hooke’s law. These can be derived from F=−kx using the axiomatic properties of equality, the axiomatic properties of multiplication, and other fundamental mathematical facts.
Item 4 applies to a number of common situations, such a force of constraint. For example, consider a block resting on the table, in equilibrium. We know that the tabletop must be slightly springy, just because all materials are. This allows us to understand the process whereby the block came to be in equilibrium.
We believe that the tabletop has a spring constant k that is very large, but not infinite. We assume that in equilibrium, the block deforms the tabletop by a small amount x, such that the product kx provides just enough force to counterbalance the weight of the block. We do not know the exact value of k or x, and we don’t need to know. The primary requirement is that the product kx must have the right value.
The physics here may be easier to visualize if you place a block on a not-very-taut rubbery drumhead (rather than a tabletop), so that the deformation is large enough to be seen. The magnitude of the deformation can be increased by piling additional weight on the block, and/or by pushing on the block by hand.
Note that when we say a force is a “force of constraint”, that does not make it different from any other force. A force is a force. It does what it does. When we say “of constraint” it is just a hint as to how we expect to calculate the force, probably calculating the force in terms of positions rather than vice versa.
The F=ma equation is shorthand for F(t)=ma(t), and means the force at a given time is proportional to the acceleration at the very same time.2 F is a vector, ma is a vector, and the equation states that at any given time, these two vectors are equal. Here equal means the familiar equality relationship discussed in section 4.
With remarkably few exceptions, the laws of physics are invariant under a reversal of the time variable. F(t)=ma(t) has the same meaning as F(−t)=ma(−t).
Item #1 (the thermodynamics exception) is the only one I can think of that is even remotely relevant to a first-year mechanics course.
Symmetry arguments contribute a lot to the power (and the elegance) of physics. Lying to students about the time-reversal symmetry of F=ma and the other laws of mechanics is unnecessary and unhelpful, to say the least.
Sometimes there are scenarios where something involving a force truly causes something involving an acceleration (and not vice versa). But you need to deduce this based on details of the scenario other than F=ma, because there is nothing in the F=ma equation that specifies a direction of causation.
On the other side of the same coin, sometimes there are scenarios where something involving an acceleration truly causes something involving a force (and not vice versa). But you need to deduce this based on details of the scenario other than F=ma.
Now, regarding causation itself, various views have been expressed. Here is the plain and simple version. This is the version I recommend.
There is a very simple syllogism:
These conclusions are inescapable, given the known laws of motion, and the foregoing major premise.
If a student asks whether F causes ma or vice versa, the answer should be: “You can’t have F without ma, and you can’t have ma without F. The relationship between them is called ’equality’ and is not properly called ’causality’. Equality is symmetric; causality is asymmetric.”
Here is the stretched version. Some people seem to favor this version. I don’t recommend it, but I can tolerate it:
In this version, causes can precede effects or coincide with effects.
As always, the F=ma equation is symmetric. F(t)=ma(t).
In this version, we choose to say that F is “caused” by ma. By the same token, we say that a is “caused” by F/m.
The relationship between F and ma remains symmetric.
Causes strictly precede effects (same as section 9.3).
Big-Endians adjoin to the laws of motion an arbitrary assertion that accelerations are caused by forces.
Little-Endians adjoin to the laws of motion an arbitrary assertion that forces are caused by accelerations.
Each of these is blatantly inconsistent with the known laws of motion, starting with F(t)=ma(t).
(See reference 8 for more about the original holy war between the Big-Endians and the Little-Endians.)
Causes precede effects, or coincide with effects (same section 9.4).
Again, Big-Endians adjoin to the laws of motion an arbitrary assertion that accelerations are caused by forces and not vice versa.
Again, Little-Endians adjoin the opposite assertion, that forces are caused by accelerations and not vice versa.
In this version, neither assertion is false; both are vacuous. Both are distasteful, because they imply that the laws of physics are less symmetric than they really are.
Neither can be deduced from any useful theoretical principle. There is no experiment a Big-Endian can do to convince a Little-Endian, or vice versa.
Any physically-relevant calculation you can do using such an assertion can be done equally well without it.
Big-Endians can employ PbBA (Proof by Bold Assertion): “it is manifestly obvious that accelerations are caused by forces” but we reject this as totally unscientific.
Big-Endians can appeal to scripture: “accelerations are caused by forces because So-And-So said so” but we reject this as totally unscientific.
Heretofore, the “=” sign signified mathematical equality (an equivalence relation, as discussed in section 4)
However, in this version we let it signify an assignment operator. That is, it represents the relationship “calculated from” rather than “equal to” or “caused by”. This is not an equivalence relation.
In this version, it is easy to come up with examples where F should be calculated from ma, and it is also easy to come up with examples where a should be calculated from F/m. Here are two examples that show the contrast:
|Example: F calculated from ma||Example: a calculated from F/m|
|Suppose we have a turntable (aka merry-go-round) rotating a a fixed rate. We mark a particular point P on the turntable, fixed relative to the turntable. There is a puck sitting at point P. We can calculate the acceleration a of this point, based on the rotational properties of the turntable. We can then multiply by the mass m of the puck and predict the force F necessary to hold the puck in place. This is an example where F is quite naturally calculated from ma.||Suppose we have a rocket of known mass being propelled by a rocket-motor of known thrust. Then we calculate the acceleration from the mass and the force.|
|In this example, it is also possible to work backwards, measuring F and m and using that to calculate the acceleration a of the special point P. However, it would be perverse to suggest that F/m was the cause of a (and not vice versa), because you can remove the puck entirely and point P would continue to accelerate just the same.|
Just because things are calculated in a given order does not mean that they happen in that order. In fact, F always happens at exactly the same time as ma, no matter how you choose to calculate things.
The F=ma equation cannot shed any light on causality relationships.
Some other computer languages have the advantage of using a less symmetric-looking symbol. They write F := m*a to signify that F is calculated from m times a. This is a conceptual advantage, and you should always think about the “calculated-from” operator as being not symmetric, even if/when it is written using a symmetric-looking symbol.
In many cases when somebody asks “why does X happen” the question is open to two interpretations: it could truly be asking for the cause of X ... or it could be asking how we know X will happen.
To repeat: In many cases (but not all), the question “why X” should be deflected. It should be transmuted into something more like:
Here’s an example of a sketchy but more-or-less reasonable statement about causation: “I know Jefferson’s inauguration happened in 1801, because the Louisiana Purchase was signed in 1803”. The point here is that if you diagram the sentence, the “because” applies to the knowing, not to the happening. The Louisiana Purchase did not cause the inauguration! It does not explain the inauguration ... but it does explain how I know the date.
Feynman made the point that a chain of deductions need not be ordered the way a chain of causation is; see reference 9.
There are at least four ideas in play here:
Each of these ideas is important, but they are definitely not the same idea.
Many of the online papers mentioned below are in PostScript format. You might find Ghostscript and Ghostview very useful for viewing such documents. The programs are available for free from: http://pages.cs.wisc.edu/~ghost/
Copyright © 2003 jsd