The social sciences, I’ve come to believe, don’t have a coherent concept of causation. To talk about ‘causation’ we need to have a boundary on cause and effect. I reflect on what this means for studying causation.

Blair Fix

For the last few weeks, I've been thinking about causation in the social
sciences. As with many instances of reflection, this was prompted by
rejection. A political economy journal recently rejected a paper that I
had submitted.

The paper (available
[here](https://osf.io/preprints/socarxiv/t8muy/){target="_blank"
rel="noopener"}) studied the correlation between hierarchical power and
income. Throughout the paper, I stressed that I was dealing with
*correlation only*. So I thought I was safe from the 'causal police' (I
thank [Steve
Dahlke](https://twitter.com/Steve_Dahlke/status/1141692165132656641){target="_blank"
rel="noopener"} for this term). But I was wrong.

Among the minutia of the rejection letter was the following sentence
that will be forever seared in my memory. "*Correlation, by itself,*"
the reviewer informed me, "*is not of scientific interest*."

I was (and am) infuriated and astonished at this response. I'm
infuriated that such a foolish response could be grounds for rejecting a
paper. And I'm astonished that a scientist could say such a thing.

Correlation, in my view, is the backbone of science. Yes, science is
ultimately about finding causes. But to *discover* the cause of
something, we need to start somewhere. And that somewhere is usually
*correlation*.

If correlation is "not of scientific interest", we need to throw out a
huge part of the scientific literature. We shouldn't publish the
correlation between smoking and lung cancer. Nor should we publish the
correlation between global temperature and carbon dioxide concentration.
Neither of these correlations can be definitely (as in lacking any
doubt) shown to be causal. So by the reviewer's logic, these
correlations are of no interest. I hope you see how ludicrous this
thinking is.

Now that I've gotten the indignation off my chest, I promise that the
rest of this post won't be a rant about a rejected paper. Instead, I'm
going to write about some reflection that was prompted by the reviewers
remarks.

Over the last few weeks I've been asking myself --- what do social
scientists mean when they say that x causes y? I've concluded that
social scientists don't have a coherent definition of causation. The
problem, as I see it, is that we can't talk about causation in a complex
system without drawing *boundaries*. Without boundaries, we get an
infinite regression of cause and effect.

As social scientists, we need to rethink what we mean by 'causation'. To
be coherent, our concept of causation needs to come with a boundary that
defines the limits of cause and effect.

### Notating causation

Much of my reflection here is inspired by the work of Judea Pearl. In
[*The Book of
Why*](https://www.goodreads.com/book/show/36204378-the-book-of-why){target="_blank"
rel="noopener"}, Pearl introduces a convenient way to notate causation.
To say that A causes B, we write:

::: {style="text-align:center;"}
A [ \longrightarrow ]{.katex-eq katex-display="false"} B
:::

While simple, this causal notation is a powerful tool. It allows us to
visualize causation in ways that would otherwise be difficult to think
about.

Here's a more complicated example:

::: {style="text-align:center;"}
A [ \longrightarrow ]{.katex-eq katex-display="false"} B [
\longrightarrow ]{.katex-eq katex-display="false"} C
:::

Here A causes C, but the effect is *mediated* by B. To explain
mediation, Judea Pearl uses the example of citrus fruit and scurvy. We
know that eating citrus fruit prevents scurvy. But it's not the fruit
*per se* that prevents scurvy. Scurvy is caused by a vitamin C
deficiency. Citrus fruit prevents scurvy because it contains vitamin C.
In Pearl's language, we say that vitamin C 'mediates' the relation
between citrus fruit and scurvy:

::: {style="text-align:center;"}
citrus fruit [ \longrightarrow ]{.katex-eq katex-display="false"}
vitamin C [ \longrightarrow ]{.katex-eq katex-display="false"} prevents
scurvy
:::

The concept of mediation is important. Let's look at how it applies to
the social sciences.

### Mediating variables in the social sciences

My discussion of mediating variables in the social sciences will focus
on income, because that's what I know best. I'll begin with the
neoclassical theory of income.

According to neoclassical economics, individual income is caused by
productivity. And this productivity, in turn, is caused by *human
capital* (a stock of skills and knowledge endowed in individuals).
Putting the two together, we get the following chain of causation:

::: {style="text-align:center;"}
human capital [ \longrightarrow ]{.katex-eq katex-display="false"}
productivity [ \longrightarrow ]{.katex-eq katex-display="false"} income
:::

There are many problems with this causal chain. For starters, the
mediator (productivity) doesn't vary enough to explain differences in
income. Then there's the fact that 'human capital' has no coherent
definition, making it hard to measure. I've written
[here](https://p.feedblitz.com/t3.asp?/973841/36917359/6341111_/www.paecon.net/PAEReview/issue86/Fix86.pdf){target="_blank"
rel="noopener"} about the many problems with human capital theory.

What is most shocking about human capital theory, I've realized, is not
what the theory includes. It's what it *excludes*. Human capital theory
excludes the social environment! The theory assumes that an individual's
income is explained entirely by *personal* traits.

I hope you see the absurdity of this thinking. Humans are *social*
animals. Our social environment pervades every aspect of our behavior,
including how we distribute resources. In other words, *culture*
mediates our behavior.

### Culture as a mediator

To illustrate culture as a mediator, let's use the example of education
and income.

In the United States, individuals with more education tend to earn more
money. Does this mean that education *causes* greater income? That's
what neoclassical economists have concluded. Educated people are more
productive, economists say, so they earn more money:

::: {style="text-align:center;"}
education [ \longrightarrow ]{.katex-eq katex-display="false"}
productivity [ \longrightarrow ]{.katex-eq katex-display="false"} income
:::

Here productivity mediates the relation between education and income.
But let's consider an alternative. What if *culture* mediates this
relation? What if it's our *ideas* about education (i.e. our values)
that cause income to increase with education. We'd write this cultural
mediation as:

::: {style="text-align:center;"}
education [ \longrightarrow ]{.katex-eq katex-display="false"} culture [
\longrightarrow ]{.katex-eq katex-display="false"} income
:::

It's easy to imagine why this might be true. Suppose we take a
PhD-trained scientist and drop him/her into a society of hunter gathers.
Does the scientist continue to earn a pay premium because of his/her
extensive education? Unlikely. The hunter gatherers don't give a damn
about the scientist's knowledge. And so the hunter gatherers are
unlikely to let the scientist take a disproportionate chunk of the
resource pie.

The point is simple. If a society values education, then education will
affect income. But if a society does *not* value education, then
education won't affect income. So it is our *ideas* about education that
cause education to affect income.

Does this mean that if we understand the relevant cultural ideas, we
completely understand the cause of income? Not really. We still have to
explain *where* the ideas came from. In other words, what caused the
culture that leads education to affect income? As we expand the scope of
analysis, we get an infinite regression of causation.

### Causation requires a boundary definition

To talk coherently about causation, we need a boundary that limits the
concept of 'cause'. In the social sciences, I can think of 3 useful
boundaries.

#### Boundary 1: Culture as a given

The simplest boundary is to exclude culture from our analysis. For
instance, we might say that in the United States in 1970, an additional
year of education 'caused' individual income to increase by 10%, on
average.

Under this causal boundary, we take the cultural context (of the US in
1970) for granted. Although it's rarely stated, this is the causal
boundary used by most social scientists. And it's easy to see why. When
you take culture for granted, it's easy to infer causation using
regressions. Run some multivariate regressions, control for some
'confounders', and bam ... you show that education 'causes' income to
increase.

I have no problem with this limited notion of causation, as long as the
boundaries are stated explicitly. However, they rarely are. Worse,
social scientists often uses inferences from a limited causal boundary
to make sweeping generalizations.

When we take culture as a given, we need to report causation as follows:
"x causes y, *given culture z*." But all too often, the "given culture
z" gets dropped, leaving the seemingly universal "x causes y".

#### Boundary 2: Culture as a mediator

Let's expand our boundary of causation. Now let's admit that our ideas
affect our actions. When we admit that culture mediates behavior, we no
longer say that education 'causes' greater income. Now there's an extra
step: culture causes education to affect income.

But not only do we add an extra step to the causal chain, we also make
the analysis more expansive. To study the effect of culture, we need to
look at *many* different social settings (not just one). For instance,
the returns to education vary greatly between countries. Why? What
aspect of culture explains this variation?

This is a difficult question to untangle, which is why few social
scientists include culture in the boundary of causation. (They may
allude to culture in the discussion, but it's rarely part of the
regression table).

#### Boundary 3: Culture is to be explained

Supposing that we understand how culture affects our behaviour, the next
step is to explain *culture itself*. When we use this causal boundary,
we no longer say that education 'causes' greater income. And we no
longer say that culture causes education to affect income. Instead, we
say that phenomenon x causes the culture that leads education to affect
income. (You can see how torturous this gets).

This is obviously the most difficult causal boundary. Genetic or
geographic determinism aside, there is no simple way to explain culture.
In fact, it's not even clear that culture has an external explanation.
Culture can be its own cause! In other words there is a feedback between
ideas and behavior that is impossible to untangle into linear causation.

### Causal boundaries and linear causation

My experience is that many social scientists are deeply wedded to a
linear concept of causation. In causal notation, linear causation is a
unidirectional arrow. A causes B:

::: {style="text-align:center;"}
A [ \longrightarrow ]{.katex-eq katex-display="false"} B
:::

Those trained in system dynamics, however, know that causation can
involve feedback. A causes B, and B causes A:

::: {style="text-align:center;"}
A [ \longleftrightarrow ]{.katex-eq katex-display="false"} B
:::

Here's a general principle that I'm going to throw out there. Linear
causation becomes less and less useful as we expand our boundary of
causation.

To illustrate, let's return to education and income. If we exclude
culture, it's fine to say that education causes greater income. This is
linear causation at its simplest. But once we include culture in the
boundary of causation, things become less simple. Ideas affect our
behavior. But our behavior also affects our ideas. In the most expansive
boundary for causation (when culture is to be explained) we admit that
there is a complex feedback between ideas and behavior. And so linear
notions of causation become almost useless.

### Lessons?

I've learned a few lessons from this reflection on causation. First, the
longer I think about causation, *the more confused I become*. Second, I
think that training on the philosophy of causation needs to be a basic
part of science education. And no, I don't mean how to infer causation
from regression tables. I mean that science education should tackle the
big questions about causation. Judea Pearl tackles these questions, and
I highly recommend you read his work.

OK, let's conclude this reflection. While I've confused myself while
writing this post, I think there's at least one cogent lesson. The next
time a social scientist tells me that x causes y, I'm going to ask them:
"what is your boundary of causation?"

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For the last few weeks, I’ve been thinking about causation in the social sciences. As with many instances of reflection, this was prompted by rejection. A political economy journal recently rejected a paper that I had submitted. The paper (available here) studied the correlation between hierarchical power and income.

Messaggi di Rogue Scholar

Rethinking Causation in the Social Sciences