The problem with philosophy

(Epistemic status: I have a high credence that I’m going to disagree with large parts of this in the future, but it all seems right to me at present. I know that’s non-Bayesian, but it’s still true.)

Philosophy is great. Some of the clearest thinkers and most rational people I know come out of philosophy, and many of my biggest worldview-changing moments have come directly from philosophers. So why is it that so many scientists seem to feel contempt towards philosophers and condescension towards their intellectual domain? I can actually occasionally relate to the irritation, and I think I understand where some of it comes from.

Every so often, a domain of thought within philosophy breaks off from the rest of philosophy and enters the sciences. Usually when this occurs, the subfield (which had previously been stagnant and unsuccessful in its attempts to make progress) is swiftly revolutionized and most of the previous problems in the field are promptly solved.

Unfortunately, what also often happens is that the philosophers that were previously working in the field are often unaware of or ignore the change in their field, and end up wasting a lot of time and looking pretty silly. Sometimes they even explicitly challenge the scientists at the forefront of this revolution, like Henri Bergson did with Einstein after he came out with his pesky new theory of time that swept away much of the past work of philosophers in one fell swoop.

Next you get a generation of philosophy students that are taught a bunch of obsolete theories, and they are later blindsided when they encounter scientists that inform them that the problems they’re working on have been solved decades ago. And by this point the scientists have left the philosophers so far in the dust that the typical philosophy student is incapable of understanding the answers to their questions without learning a whole new area of math or something. Thus usually the philosophers just keep on their merry way, asking each other increasingly abstruse questions and working harder and harder to justify their own intellectual efforts. Meanwhile scientists move further and further beyond them, occasionally dropping in to laugh at their colleagues that are stuck back in the Middle Ages.

Part of why this happens is structural. Philosophy is the womb inside which develops the seeds of great revolutions of knowledge. It is where ideas germinate and turn from vague intuitions and hotbeds of conceptual confusion into precisely answerable questions. And once these questions are answerable, the scientists and mathematicians sweep in and make short work of them, finishing the job that philosophy started.

I think that one area in which this has happened is causality.

Statisticians now know how to model causal relationships, how to distinguish them from mere regularities, how to deal with common causes and causal pre-emption, how to assess counterfactuals and assign precise probabilities to these statements, and how to compare different causal models and determine which is most likely to be true.

(By the way, guess where I came to be aware of all of this? It wasn’t in the metaphysics class in which we spent over a month discussing the philosophy of causation. No, it was a statistician friend of mine who showed me a book by Judea Pearl and encouraged me to get up to date with modern methods of causal modeling.)

Causality as a subject has firmly and fully left the domain of philosophy. We now have a fully fleshed out framework of causal reasoning that is capable of answering all of the ancient philosophical questions and more. This is not to say that there is no more work to be done on understanding causality… just that this work is not going to be done by philosophers. It is going to be done by statisticians, computer scientists, and physicists.

Another area besides causality where I think this has happened is epistemology. Modern advances in epistemology are not coming out of the philosophy departments. They’re coming out of machine learning institutes and artificial intelligence researchers, who are working on turning the question of “how do we optimally come to justified beliefs in a posteriori matters?” into precise code-able algorithms.

I’m thinking about doing a series of posts called “X for philosophers”, in which I take an area of inquiry that has historically been the domain of philosophy, and explain how modern scientific methods have solved or are solving the central questions in this area.

For instance, here’s a brief guide to how to translate all the standard types of causal statements philosophers have debated for centuries into simple algebra problems:

Causal model

An ordered triple of exogenous variables, endogenous variables, and structural equations for each endogenous variable

Causal diagram

A directed acyclic graph representing a causal model, whose nodes represent the endogenous variables and whose edges represent the structural equations

Causal relationship

A directed edge in a causal diagram

Causal intervention

A mutilated causal diagram in which the edges between the intervened node and all its parent nodes are removed

Probability of A if B

P(A | B)

Probability of A if we intervene on B

P(A | do B) = P(AB)

Probability that A would have happened, had B happened

P(AB | -B)

Probability that B is a necessary cause of A

P(-A-B | A, B)

Probability that B is a sufficient cause of A

P(AB | -A, -B)

Right there is the guide to understanding the nature of causal relationships, and assessing the precise probabilities of causal conditional statements, counterfactual statements, and statements of necessary and sufficient causation.

To most philosophy students and professors, what I’ve written is probably chicken-scratch. But it is crucially important for them in order to not become obsolete in their causal thinking.

There’s an unhealthy tendency amongst some philosophers to, when presented with such chicken-scratch, dismiss it as not being philosophical enough and then go back to reading David Lewis’s arguments for the existence of possible worlds. It is this that, I think, is a large part of the scientist’s tendency to dismiss philosophers as outdated and intellectually behind the times. And it’s hard not to agree with them when you’ve seen both the crystal-clear beauty of formal causal modeling, and also the debates over things like how to evaluate the actual “distance” between possible worlds.

Artificial intelligence researcher extraordinaire Stuart Russell has said that he knew immediately upon reading Pearl’s book on causal modeling that it was going to change the world. Philosophy professors should either teach graph theory and Bayesian networks, or they should not make a pretense of teaching causality at all.

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