Macro-causality and social science

Consider a little science experiment we’ve all done, to find out if a switch controls a light. How many data points does it usually take to convince you? Not many! Even if you didn’t do a randomized trial yourself, and observed somebody else manipulating the switch you’d figure it out pretty quickly. This type of science is easy!

One thing that makes this easy, is that you already know the right level of abstraction for the problem: what a switch is, that it has two states, and that it often controls things like lights. What if the data you had was actually a million variables, representing the state of every atom in the switch, or in the room?

Even though, technically, this data includes everything about the state of the switch, it’s overkill and not directly useful. For it to be useful, it would be better if you could boil it back down to a “macro” description that is just a switch with two states. Unfortunately, it’s not very easy to go from the micro description to the macro one. One reason for this is the “curse of dimensionality”: a few samples of a million dimensional space is considered very under-sampled, and directly applying machine learning methods using this type of data often leads to unreliable results.

As an example of another thing that could go wrong, imagine that we detect, with p<0.000001, that atom 173 is a perfect predictor of the light being on or off. Headlines immediately proclaim the important role of atom 173 in production of light. A complicated apparatus to manipulate atom 173 is devised only to reveal… nothing. The role of this atom is meaningless in isolation from the rest of the switch. And this hints at the meaning of “macro-causality” – to identify (simple) causal effects, we first have describe our system at the right level of abstraction. Then we can say that flipping the switch causes the light to go on. There is a causal story involving all the atoms in the switch, electrons, etc., but this is not very useful.

Social science’s micro-macro problem

Social science has a similar micro-macro problem. If we get “micro” data about every decision an individual makes, is it possible to recover the macro state of the individual? You could ask the same where the micro-variables are individuals and you want to know the state of an organization like a company.

Currently, we use expert intuition to come up with macro-states. For individuals, this might be a theory of personality or mood and include states like extroversion, or a test of depression, etc. After dreaming up a good idea for a macro-state, the expert makes up some questions that they think reflect that factor. Finally, they ask an individual to answer these questions. There are many places where things can go wrong in this process. Do experts really know all the macro states for individuals, or organizations? Do the questions they come up with accurately gauge these states? Are the answers that individuals provide a reliable measure?

Most of social science is about answering the last two questions. We assume we know what the right macro-states are (mood, personality, etc.) and we just need better ways to measure them. What if we are wrong? There may be hidden states underlying human behavior that remain unknown. This brings us back to the light switch example. If we can identify the right description of our system (a switch with two states), experimenting with the effects of the switch is easy.

The mapping from micro to macro is sometimes called “coarse-graining” by physicists. Unfortunately coarse-graining in physics usually relies on reasoning based on the physical laws of the universe, allowing us, for instance, to go from describing a box of atoms with many degrees of freedom to a simple description involving just three macro-variables: volume, pressure, and temperature.

Finding ways to automate coarse-graining for complex systems, as arise in social science, is one of the main goals of my research. One simple idea, the CorEx principle, has motivated a lot of this work. The principle says that “a good macro-variable description should explain most of the relationships among the micro-variables.” We have gotten some mileage from this idea, finding useful structure in gene expression data, social science data, and (in ongoing work) brain imaging, but I suspect it’s far from enough to completely solve this problem.  Coarse-graining that allows us to simplify the causal description of our system (as in the light switch example) seems like a fruitful angle to push this research further, and I hope to make or see more progress on this question in the future. (A few ideas about this that I’m aware of are here: 1 2 3 4 5. I would love to know of things I’ve missed! )


Source: Apparent Horizons

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