[From Rick Marken (2011.06.30.2030 PDT)]
Bill Powers (2011.06.30.0630 mdt)–
BP: Behind the idea of causation is a conviction that you can isolate just one significant cause, while the other variables are unimportant and have only minor effects.
I don’t think that’s really true. I think the idea behind causation (as it is used in research) is that you can see whether a variable (or variables – most research involves manipulation of several variables simultaneously) has any effect at all on another variable and, if so, what the nature of that effect (the nature of the functional relationship between variables) is. Importance is dealt with in terms of the proportion of variance in the DV that is accounted for by the IV. Of course, in conventional research this approach to measuring importance assumes a causal model (the General Linear Model of statistics). We know, of course, that if you are dealing with a control system, the appropriate way to evaluate “importance” is in terms of how well a closed-loop model, taking the IV(s) as a disturbance to a CV, accounts for the variance in the DV.
It’s a naive wish for simplicity that creates the concept of “the cause of B.” In general there is no one cause of B. The only way to make it seem that A1 causes B is to keep all the other A’s from showing their natural variations. This is a very handy way to stack the deck to help a weak theory. You simply hold all variables constant but the one your theory says is the important one. It then is guaranteed to be the only important one.
Then how do you go about doing research? Just observe naturally occurring variations in variables and relationships between them? No experiments?
BP: In PCT, or system analysis in general, we don’t have to keep all the system variables but one constant. We have to let qi, p, e, and qo vary in order to have a working control system.
Of course. And qi and qo are the only observable variables in that list; and we don’t just let qi vary naturally; we vary it (or try to vary it) by manipulating an independent variable, the disturbance – the other variable that we can observe; kind of an important one.
If we can measure d and r, we don’t have to keep them constant, either.
We can’t keep d constant and do an experiment; d must vary or there is no experiment. And in an experiment d is manipulated by the experimenter. In control system studies r can’t be held constant or manipulated; it’s the “wild card” in research on control; something people who study physical systems don’t have to worry about. While me can measure r (as you have shown) we can’t manipulate (vary) it.
We simply record the values of all the variables we can find, and show that all the dependent variables can be calculated from the values of d and r, the independent variables. If there are unpredicted variations, they must arise because of other independent variables we have failed to notice or that are too numerous to keep track of.
You seem to be saying that experiments can be done without experimental control. But I don’t think that’s true. When we manipulate d (the IV in our experiments) we are also, at least implicitly, holding other variables constant, which happens so naturally in our tracking tasks that it goes unnoticed. But, for example, you would implicitly control the type of disturbance waveform you use in a study of the effect of disturbance frequency on control performance. You wouldn’t use a sine wave disturbances for the low frequency and noise waveforms for the high frequency disturbances, would you? You are such a good, natural researcher that you just automatically design your experiments so that there is as little confounding as possible.
The less unpredicted variation there is in the dependent variables, the more sure we can be that we have accounted for all the main variables of importance.
That’s exactly how the “importance” of a presumed “causal” variable is evaluated in conventional research. They measure predicted rather than unpredicted variation but the idea is the same. The difference between conventional and control research is that in conventional research the variation is predicted using an open loop model while in control research the variation is predicted using a closed loop model. Indeed, this was the subject of my last paper (still being reviewed) and will be the topic of my talk if I give one at the meeting. What I found is that a closed loop model accounts for more of the variance in the dependent variable than does an open loop model in an experimental situation where it appears that there is an open-loop connection between d and qo.
We can use the measured values of the variables to deduce the forms, or at least plausible forms, of the various functions connecting the variables. As long as the equations containing those functions and variables continue to predict the future values of the dependent variables, we can be satisfied that we understand the system well enough for now, without having to designate any one cause or effect.
And that’s exactly what I have done with the “object interception” data that I’ve been analyzing. In those experiments the disturbance is the path in 3-space of the object to be intercepted. There is no obvious control of other variables but there was implicit control, as in the tracking experiments. Most obvious is that all the trials were conducted in the same space using the same object to be intercepted. I used the observed values of the paths of the objects in 3-space in a control model to predict (with startling accuracy sometimes) the position of the person trying to intercept the object.
So I don’t see any use for the term “cause” in a scientific discussion of behavior, or anything else for that matter. It’s an ancient concept which is no longer needed. Informal usages, of course, will remain, and we will have to deal with others who still think the term is meaningful, but when we want precision we can talk about functional relationships among multiple variables. We no longer have to hold all else equal from the Big Bang to the present.
I agree that “cause” is a problematic concept. But it’s at the heart of conventional psychological research and I don’t think I’d be able to communicate with my colleagues too well if I dropped it. I actually find that talking about the “causal model” of behavior as the basis of psychological research provides a nice segue into a discussion of the closed loop model. It seemed to resonate quite well with the students in my Research Methods class.
PS. I’ve been moved to tears by your discussion with Adam. Thanks.