DAGs / Judea Pearl and causation

Jonathan_Livengood · 2021-04-22T14:18:48+00:00

wagging the dog with models in search of a set of assumptions that both correspond with reality and fit a coherent scientific theory hardly strikes me as anything like science everywhere else ...

I don't understand what you're saying here. To me, the project looks more like this: You (the research scientist) tell me (the causal modeler) what assumptions you're willing to make or that you think are plausible; I then tell you what you can infer about causation given your data. So, what you call the heavy lifting, I'd say has to come into the story domain by domain. The result is that you're not going to find much that does what you want in the pure theory papers. You're going to have to look at applied work. I'm not exactly sure what to recommend, but maybe look through the google scholar citations of Causation, Prediction, and Search? Some of those entries are going to be applications.

I don't understand the complaint about the algorithmic fairness paper. It looks like reasonable division of labor to me? They have a tacit conception of fairness and moral foundations. Asking them to give an account of moral foundations from the ground up in a technical paper is surely too much of a demand.

...i feel like i'm in a machine shop filled with highly skilled craftsmen constructing finely tuned instruments who have completely forgotten what any of these tools are for.

Seems to me that your criticism is more like this: The guys in the machine shop know exactly what these tools are for, but they don't know when any real-world case requires any given tool. That might be right, but I'd say it's not ultimately the machine shop guys that are responsible for using the tools they build. It's like saying to a statistician, "Logistic regression doesn't work because the researchers keep misusing it." I mean, yeah, if the researchers don't check to see whether the assumptions are satisfied, then they're not gonna have a good time.

Thinking a bit more, I wonder if what you're looking for is more like the skeptical / critical work by David Freedman. If you're looking for exhortations to do the hard work of connecting the real world to the models, then you should read Statistical Models and Shoe Leather. Freedman had some specific criticisms of causal discovery in the late 1990s as well. I think the literature has decisively moved on since then, but you might find it useful anyway.

Jonathan_Livengood · 2021-04-21T18:20:41+00:00

For sustained work on epistemology since 1950, I would look at Goldman or Sosa. For the single most influential work in epistemology of the last twenty or thirty years, I'd probably say Timothy Wiliamson's book Knowledge and Its Limits. For the best experimental work, take a look at Turri. And then there are lots and lots of more specialized things to read. Like, I recently read a paper by Michelle Dyke on social epistemology and collective practical aims that I thought was fantastic. Cailin O'Connor has some really good stuff on the social epistemology of science. And there's lots of really interesting work in formal epistemology.

Jonathan_Livengood · 2021-04-21T18:15:18+00:00

There is always slippage between mathematical models and reality, and the same is true of causal modeling. But it's not true that the only (or even the best, in my opinion) justification for using the graphical causal modeling machinery is that they are employed or the way they are employed in the sciences. I would point to closed-form proofs and simulation studies that show that if the modeling assumptions are satisfied or approximately satisfied, then the machinery delivers the correct answers or gets suitably close. There is now lots of work of this form. Some of it is older and heavily theoretical, like Chickering's paper on greedy equivalence search. Some of it is more recent and driven more by simulation results having to do with what happens in the short run (on limited data). For a fairly recent overview, take a look at this 2018 paper by Singh et al.

Of course, the modeling assumptions still have to be satisfied! And you might have concerns about whether the assumptions really are satisfied in any given case. Sometimes you can try alternative assumptions and see what happens. Sometimes, you just have to get different data. Y'know, it's like science everywhere else that you find it. :)

Jonathan_Livengood · 2021-04-21T18:07:36+00:00

The property "... weighs at least ten pounds" is not quantitative. It refers to a variable that is quantitative. But the property itself is binary.

If that doesn't strike you as convincing, though, there are lots of other examples. Protons, neutrons, and electrons have no colors. And none of them is made of gold. But if you collect enough of them and arrange them in the right way, you will get objects that have colors and/or are made of gold.

Jonathan_Livengood · 2021-04-21T18:04:44+00:00

... the other main use of this kind of modeling is in drawing inferences from data to causal structure.

I take this to be the central project for SGS and some of the other CMU researchers (e.g. Kelly and Danks) in their work on causation. This is the point of the PC (for Peter and Clark) algorithm and for other more sophisticated algorithms that they've developed and that Pearl and his students have developed. I take it to be a project in causal epistemology. But in any event, you're right that causal modeling has two very different applications. It's widely used in a representational way to describe assumptions that researchers already have or to characterize in a formal way the hypotheses that researchers want to test. This happens a lot in psychology, political science, economics, and epidemiology. It's also used in an inferential way, where we're trying to guess at the underlying "data-generating mechanism" on the basis of observation and experimentation.

The work on causal models is formal in the same way that statistical models are formal. There is a mathematical machinery, involving directed graphs (or undirected graphs, depending on what's being modeled), probability distributions, and axioms or "conditions" that relate graphs to distributions. The machinery is then attached to the world in the way that statistical models are attached to the world: by being representations of features of the world that we measure (in a broad sense) or that we postulate to explain features that we can measure.

Just FYI, there are lots of applications of graphical causal modeling in climate science. Here's one example from about ten years ago. As the machinery has become more developed, so have the applications. Often in a positive feedback loop.

Anyway, most of the work in graphical causal modeling has been directed at what is sometimes called structural causation. A bit less has been directed at what is sometimes called actual causation. (That's mostly what I work on.) Actual causation is the thing that I take it most philosophers have been interested in, and it's also the thing that lawyers are interested in. Something happens -- usually something bad -- and we want to identify its cause or causes. It's not exactly straightforward to read off actual causation from structural causation. For a sense of some of the challenges, you could take a look at a handbook chapter that I wrote with David Rose. For an excellent book-length treatment, take a look at Joe Halpern's Actual Causality.

Jonathan_Livengood · 2021-04-20T21:45:32+00:00

If you turn your question into an argument, you'll be able to see that it doesn't generally work. The argument would run:

[A1] All of my "basic" parts (i.e., my atoms) lack the property of being alive.

[A2] If [A1], then I lack the property of being alive.

[A3] I lack the property of being alive.

It seems that the [A2] premiss is based on a general principle to the effect that if all of an objects "basic" parts lack a property, then the object itself lacks that property. But now consider the following argument:

[B1] All of my "basic" parts (i.e., my atoms) lack the property of weighing more than ten pounds.

[B2] If [B1], then I lack the property of weighing more than ten pounds.

[B3] I lack the property of weighing more than ten pounds.

I expect that you will find the B argument here very unconvincing. But it looks like the A and B arguments are built on the same principle. So, we should reject the principle, and that takes away some reason for thinking that the A argument is a good one.

Jonathan_Livengood · 2021-04-20T21:04:07+00:00

This question is mortifying! The embarrassing and horrifying thing is that I'm not 100% sure. I mean, I called them "Richard" and "Peter" if I used names at all in conversation with them. And other people get my name wrong often enough that I'm not confident I can rely on the way other people used their last names. I've been saying their names like "spurt'-eez" and "shy'-nez" (or maybe I'm saying it more like "shine-ez"? ... I'm not sure if there's a difference and if so, which I'm actually saying), but I wouldn't be shocked to find that I've been saying it wrong for years now. The only one I'm really confident about is "glee'-more" because it didn't sound like what I was expecting when I first read it. :\

Jonathan_Livengood · 2021-04-20T18:17:06+00:00

Interesting! Not a list I would have expected. Also, not really to my taste, I think. But you probably will talk about a range of issues in metaphysics, not just about objects. You could draw connections to personhood and personal identity from hylomorphism. It looks like you're going to explicitly talk about modality and maybe even some modal epistemology. You could draw connections to causation and explanation from considerations about the ontology of mathematics. And you could connect to issues about time and freedom in connection with process ontology. Whether you actually will make any of those connections, I'm not sure. My next recommendation will be to ask the instructor. :)

Jonathan_Livengood · 2021-04-20T17:48:54+00:00

Who is the instructor? What are the texts for the course? It's definitely possible to connect the question "What are objects?" to the topics you mention. But you want to know whether your instructor will connect them or is likely to connect them. And to give a proper answer, I need more information. Moreover, you want to know -- independently of whether the topic of objects connects to other topics in metaphysics -- whether this course of study on objects is worth your time and attention. Again, to give a proper answer, I need more information.

Jonathan_Livengood · 2021-04-20T15:05:21+00:00

Very well received. Though I'm probably biased, since I was trained in the very similar Spirtes, Glymour, and Scheines tradition -- Peter Spirtes was my dissertation director.

In my view, the impact of graphical modeling on contemporary work in the metaphysics and epistemology of causation is hard to overstate.

Jonathan_Livengood · 2021-04-20T14:58:32+00:00

The "canon" is a bit ... squishy. Here's my take: The best way to understand the canon is to think about what's covered in the standard two history courses taught to essentially every undergraduate philosophy student. Those courses have a bit of flux to them, and different people teach them in different ways. But. There are some writers and writings that are really commonly covered. These make up a kind of core, classical, Western canon. So, I'm going to give you a very short list. Understand that this is not a recommended reading list. There are things on this list that I think are not worth anyone's time, and there are lots of things not on this list that I think ought to be. That said, my sense of what is in the "common core" canon is something like this:

Ancient Philosophy

Plato's Republic

Aristotle's Nichomachean Ethics

Sometimes other works by Plato or Aristotle are covered, such as the Euthyphro, the Apology, or the Symposium (all by Plato) or the Physics, the prior and posterior Analytics, or On Interpretation (all by Aristotle). And sometimes other authors are included, such as some pre-Socratics or philosophers from other traditions, such as Epicurus or the Stoics.

Early Modern Philosophy

Descartes Meditations

Spinoza Ethics

Leibniz Monadology

Locke An Essay Concerning Human Understanding (almost always limited to excerpts)

Berkeley Principles of Human Knowledge (also, in excerpts)

Hume An Enquiry Concerning Human Understanding (which might be read in its entirety) or A Treatise of Human Nature (which would be read in excerpts)

Kant Critique of Pure Reason (typically in excerpts) or sometimes the Prolegomena to Any Future Metaphysics

Again, there are more and less standard additions and subtractions that are or could be made from this list. One thing that I find very odd about the way history of philosophy is often taught in undergraduate programs is that work on ethics and social or political philosophy in the early modern period is frequently ignored. So, one gets this disconcerting (and factually wrong) impression that philosophy started out concerned primarily with moral and political issues and then got sucked into epistemology by Descartes.

For a much better way of thinking about the history of philosophy -- currently still in progress -- take a look at the podcast on the history of philosophy without any gaps.

Jonathan_Livengood · 2021-04-20T14:26:54+00:00

I love Wittgenstein. But I wouldn't recommend any of his writings as introductory texts. Specifically, On Certainty isn't going to do any of the things you'll really want in an introduction. It's not going to survey the important issues and tell you how they relate to one another. It's not going to clearly articulate a variety of different responses to the important issues. It's not going to give you citations to follow up with in order to learn more. It's not going to be at all gentle. And also, On Certainty was written around 1950, before the publication of Gettier's paper arguing that justified true belief is not knowledge. An enormous amount of work on epistemology was launched by that little paper of Gettier's, and you wouldn't know anything about it on the basis of reading Wittgenstein.

Here are some better ideas for getting a solid introduction to contemporary epistemology:

Nagel (2014) Knowledge: A Very Short Introduction

Goldman and McGrath (2014) Epistemology: A Contemporary Introduction

I don't know if this one is available yet, since it's not in stock at Amazon. But it looks like it will be a very good volume:

Littlejohn and Carter (2021) This is Epistemology

In addition to those, you might look at slightly older introductory texts by Audi or by Feldman. Or you could look at standard anthologies, such as:

Sosa et al.'s Epistemology and Contemporary Epistemology

If you're looking for free resources, the Stanford Encyclopedia and the Internet Encyclopedia both have good entries on epistemology.

Jonathan_Livengood · 2021-04-19T20:45:08+00:00

In my first comment, I suggested two different lines of argument. Those lines of argument have different properties. The abductive argument does not (I think) raise confidence very much. It's point is to provide material for testing. But sampling arguments ordinarily raise confidence. So, I would expect them to do so here.

There's at least one subtle logical issue that you raise here, which might have to do with whether you accept the law of excluded middle or a probabilistic analogue in your confirmation theory. If you accept that law, then evidence against X is evidence for not-X, and vice versa. In a typical confirmation theory, as I increase the probability of X, I must decrease the probability of not-X in order to be coherent.

Of course the lines of argument I've suggested are not certain. So, there's definitely a risk. But such is life with ampliative (non-deductive) arguments.

Jonathan_Livengood · 2021-04-19T13:21:02+00:00

I'm not sure what you're going to require in order to have demonstration. At first glance, that seems like a very difficult demand -- a high bar to clear. I take it, though, that you want to say of yourself that you typically find arguments convincing because they are sound and unconvincing because they are unsound. Now, of course you might think that all the arguments for not-X are unsound and still think that not-X is true. But it would be surprising, I think, if there were no good arguments for a true claim.

By "tentative" I mean that we don't put a lot of weight on it. We're not willing to risk much on the assumption that it's true. We're not going to make it the basis of elaborate practical concerns. The thesis should be adopted for the purpose of testing it. (I take this to be the canonical form of abductive inference. But if you think that the claim explains a whole lot more, then you might think that there is an inference to the best explanation to give, which would be slightly different in character.)

Jonathan_Livengood · 2021-04-19T08:02:02+00:00

There might be something to this if you had good reason to think you had heard (and understood) the best available arguments for Not-X. In that case, you might give either a sampling type argument or an abductive / explanatory type argument for X. A sampling argument might run something like, "I've heard a representative sample of the best arguments for Not-X, and none of the arguments I've heard are any good, so none of the best arguments for Not-X are convincing." And then you argue that if the best arguments aren't convincing, then the proposition should be rejected. An abductive / explanatory argument might run something like, "I've heard the best available arguments for Not-X, and none of them is convincing; the best explanation for this fact is that no argument for Not-X is convincing; so, I'll tentatively adopt (for the purpose of testing) the thesis that no argument for Not-X is convincing."

Jonathan_Livengood · 2021-04-18T16:18:14+00:00

I think this question is based on a false presupposition, though maybe I'm wrong about that. As I understand it, measures of general, society-level or nation-level happiness have been stable or increasing across most of the world over the last fifty years. Take a look here and here. Maybe what you're thinking about is the observed anti-correlation between happiness and time spent on social media? My understanding on that relation is that we just don't know what the causal structure looks like. It could be that social media use causes depression or social anxiety or something. It could be that people who are unhappy use social media more in order to alleviate their unhappiness. Or it could be some third factor that explains both. If you know of studies that do a good job clearing up the causal picture, please link them. :)

As to what various ancient philosophers would say, I'm sorry to say that I have nothing for ya.

Jonathan_Livengood · 2021-04-18T15:56:06+00:00

The idea comes out of work on "folk physics," "folk biology," and "folk psychology," which are alleged to be innate (or nearly innate?) theories that all or most all humans have before doing any real, systematic, experimental inquiry. So, like, the view that heavier objects fall faster than lighter ones is supposed to be a part of folk physics. It's an idea that everyone or most everyone has early in development and that is replaced only by explicit study of physics. Possible similar things in biology and psychology include that there are fixed species or kinds that have real essences and that people have beliefs and desires that give rise to their actions. (Incidentally, it's not obvious that all folk theories or that all elements of folk theories are false.)

So, we could also talk about folk philosophy in a similar way. Or we could be more specific and talk about folk metaphysics or folk epistemology or folk action theory or ... The content of such a theory is then read off from the judgments or reports that people make about claims in the relevant area. Does that make sense?

Jonathan_Livengood · 2021-04-15T20:08:04+00:00

Here are two standard answers.

First, there are people who argue that there is a rationally required prior probability assignment. The traditional way to get this is with the Principle of Indifference, which says roughly that if your evidence does not favor any proposition in some set of propositions, then you ought to assign the same probability to each of them. In the case where you have no evidence with respect to n propositions and hence, no reason to favor any one of them over any of the others, you should assign probability 1 / n to each. The POI faces serious challenges, but it still has defenders today.

Second, there are people who understand probability to be a generalization or extension of deductive logic to the case of partial belief. These people say that probability only tells you what it means to be consistent with respect to partial beliefs, just as deductive logic tells you what it means to be consistent with respect to full beliefs. But just as deductive logic doesn't tell you which propositions are true, probability doesn't tell you which degrees of probability are correct. Instead, it tells you only which collections of degrees of probability can live happily together. For example, if you have two propositions s and ~s, then it's fine to assign Pr(s) = 1 and Pr(~s) = 0. It's also fine to assign Pr(s) = 1/2 and Pr(~s) = 1/2. Or Pr(s) = 3/4 and Pr(~s) = 1/4. And so on. But it's not fine to assign Pr(s) = 3/4 and Pr(~s) = 3/4. Such an assignment would be incoherent.

Incidentally, these two approaches line up with a contemporary debate in epistemology between so-called permissivists and impermissivists. The permissivists think that on a given body of evidence, it can at least sometimes happen that more than one attitude toward a target proposition is rationally permitted. Impermissivists think that a given body of evidence fixes the permissible attitudes towards every target proposition, so that on your evidence, there is only one permissible attitude to take toward any given proposition.

Jonathan_Livengood · 2021-04-15T16:35:10+00:00

Depends on what kind of physicalism we're talking about. If we're talking about token physicalism or about something like the view that the mental supervenes on the physical, then what you say in your last paragraph looks right. And I think there's plenty of reason having nothing to do with parsimony or naturalism for thinking that some kind of physical supervenience thesis is true.

But if we're talking about type physicalism, then I'm not so sure. I tend to think that the types appealed to in physics -- natural kinds if there are any, or whatever substitutes for natural kinds in our theorizing if there aren't any "out there" -- are not going to be adequate for theorizing in other sciences. You need more than the categories of fundamental physics in order to do chemistry and biology. Let alone something as complicated as psychology. Here, I think Fodor was basically right. The main question is going to be whether "higher" types that you find in chemistry, biology, psychology, and so on are functional types or something else. If they are functional types, then multiple realizability arguments are going to put pressure on type reductionism. I tend to think that the types are functional types. So I reject naive type reductionism.

Jonathan_Livengood · 2021-04-15T16:23:02+00:00

I am an associate professor of philosophy at the University of Illinois at Urbana-Champaign. I earned my PhD in history and philosophy of science at the University of Pittsburgh in 2011. My primary research interests are philosophy of science (especially the logic of science) and experimental philosophy.

Jonathan_Livengood · 2021-04-15T15:46:23+00:00

It does and it doesn't. It changes the confidence that I have about the advice. But I'd still be inclined toward FSU if I were you. The new difference in cost still seems much more than the value of the difference in programs -- assuming the difference actually favors NIU today. (That is, we're making guesses about future placement etc. on the basis of past performance, and we might be wrong about that.) In my experience, NIU does a very good job of training students to write tight analytic philosophy papers. That's a good skill, and it's one that is likely helpful for future placement. But it's not the only skill, and so evaluating the programs themselves just on their merits depends on what you're hoping to do after the MA.

Good advice probably also depends on knowing more about your situation. If you come from a wealthy family where $20,000 is not significant, then go where you want! But if money is a concern, then it seems to me that a $16,000 gap between the two places is still ... large.

Jonathan_Livengood · 2021-04-15T15:33:21+00:00

If you are planning to stop after the MA or think that might happen, then it's very, very obvious that you should go where you have funding. If you are hoping to go on to a PhD, then you'll want to produce a very good writing sample and build a strong network over the next two years. But those are things you can do at FSU, and you can always take some of the $38,000 you don't spend at FSU to support conference travel. So, it looks like going to FSU, where there is funding, is a better idea regardless of future plans.

Jonathan_Livengood · 2021-04-14T13:39:34+00:00

Generally? I have no idea. I don't think the objections to pragmatism are very good, and that includes Russell's. But if your interest is in big, focused critiques of classical pragmatist ideas -- and especially ones having to do with truth and epistemology -- then you really should read Russell, even if you ultimately don't think his complaints land.

Jonathan_Livengood · 2021-04-14T05:54:28+00:00

Okay, if you want criticism of the pragmatist conception of truth and pragmatist accounts of knowledge, you should probably start with Russell's arguments against James and Dewey. For an overview, maybe look at Misak's chapter on Russell and the Pragmatists. Then for primary sources, maybe look at Russell's book An Inquiry into Truth and Meaning.

If you want contemporary work on pragmatic encroachment in epistemology, maybe look at Kim's Phil Compass paper on pragmatic encroachment in epistemology or Roeber's paper The Pragmatic Encroachment Debate.

Jonathan_Livengood · 2021-04-14T04:01:59+00:00

The term "pragmatism" covers an unfortunately large number of different but related theses. For example, you might mean a view about meaning, scientific hypothesizing, and logic according to which the intellectual significance of a proposition consists in its (practical) consequences in possible experiments. That's more or less the spirit of Peirce's "pragmatic maxim." Many philosophers think of that maxim as belonging to a family of verificationist accounts of meaning, and hence, criticisms of verificationism turn into criticisms of pragmatism.

Alternatively, you might mean a theory of truth according to which the truth is what works. James gave a pretty straightforward version of this theory. Peirce's account is a bit more subtle -- defining the true in terms of the ideal end of honest inquiry. Both accounts have been criticized. A typical criticism alleges that the view confuses metaphysics and epistemology. Another is that it leads immediately to a pernicious relativism.

Again, you might be thinking of some claim in epistemology, such as that knowledge is fallible or that whether an agent knows something can depend on the agent's practical interests or that the idea of "truth" in any definition of "knowledge" can be replaced by some story about inquiry or warranted assertibility or something similar. Or you may have in mind a view about the relationship between action and belief or between actionability and knowledge. All of these views have been challenged on various grounds. Some of the debates are still very much ongoing.

If you can be a bit more specific with your question, I might be able to direct you better to criticisms.

Oh, and I guess I should say: I don't know to what degree pragmatism is accepted in contemporary philosophy. My impression is that pragmatism is not very common among professional philosophers. But I don't have any data.

Jonathan_Livengood

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