Reddit reviews Causality: Models, Reasoning and Inference

A high-profile prize like the Nobel Memorial Prize for Economic Sciences is not just about the accomplishments of the recipient; it signals what is valued in economics community. The prize committee considers not only the contributions of the nominees to economics as a field of inquiry, but also whether the economics community would benefit from a signal-boost for the approach that the nominees use, or the particular sub-field or topic the nominees research. If the prize was only awarded for big ideas and grand narratives, that would signal that those are the only kinds of contributions that really matter to economics--or, alternatively, that the Nobel Memorial Prize for Economic Sciences is really a niche prize dedicated to a small subset of what the economics community cares about.

I agree with you that some of the most influential economists are the ones who presented big ideas and grand narratives. Big ideas and grand narratives give a framing to an otherwise too complex world, so if along comes a well-expounded grand narrative with a core big idea at just the right time, it can take over the world--even if it's demonstrably wrong. (See: Karl Marx, "Das Kapital".)

Speaking of Karl Marx: Despite the massive misery that his works supported in the 20th century, I agree with those who say that he was actually a pretty good sociologist. He masterfully described the misery that permeated the factories of his day, and he presented a framework that attempts to explain its causes. His weakness, both as an economist and as a sociologist, is that HE DIDN'T TEST HIS THEORIES WITH A FREAKING RCT.

Sorry for finger yelling. I guess I am still raw about Karl Marx, having lived under communism.

My point is that causal relationships that appear obvious, aren't. This observation underlies the entirety of social sciences. It is also very difficult to demonstrate causal relationships. While it's possible to do so without intervention, the approach requires one to already have a good model to account for confounders, and of course measurements of all the confounders. RCTs don't. That's why they are still regarded as the gold standard of demonstrating causality.

This. People do not need to skate around discussions of causality because we have discovered ways to mitigate confounding. Specifically, causation can be pragmatically inferred from interrelationships between correlatative statistical networks, d-separation criteria, and counterfactual modeling. Further reading can be found here.

It does not, and the set of confounding variables in fitness is legion. The particular study you linked is a pure observational exercise that makes no attempt to deal with selection issues and the idea that one might find causality in the results is fantasy.

Consider checking out some causality literature, either the Ruben-Imbens thread or the Pearl devotees. Join us in believing nothing very few extant studies.

Or just read people like Andrew Gelman and dispense with learning about causality, there are ten dozen other reasons most published research findings are false.

Related.

*There is no way to determine causation from a single correlation without further assumptions. There is a large body of literature devoted to estimating causal relationships without experimental data. Here are a couple of standard textbooks in this literature.

Causality by Judea Pearl. I've been interested in tackling this book for a while. Being able to use observational probabilities to bolster causal models seems interesting and useful.

> Or that communism creates starvation (joke)

I don't think this is a joke. While causal designs would be difficult to apply, the spatio-temporal correlation is hard to ignore.


>Regarding causality- as you know that’s nearly impossible to prove in the social sciences.

Actually, these days the application of designs and approaches that provide strong support for causal claims have become quite prevalent. Some standard references-



1

2

3

4

good framework reference or a slightly heavier read

and the old classic


In fact, the Nobel prize in economics this year went to some people who have built their careers doing exactly that

It's actually become quite hard to publish in ranking journals in some fields without a convincing (causal) identification strategy.


But we digress.


>We will never be able to do an apples to apples study between heterosexual and homosexual child rearing for some of the reasons you mentioned above. (Diversity of relationship styles, not both biological parents within gay/lesbian couples)

In this case it isn't far fetched at all. The data collection for the survey data used in the study you linked could just as easily have disagregated the parents involved in same sex romantic relationships instead of pooling them. If I understood correctly, the researcher had obtained the data as a secondary source, so they didn't have control over this.

Outcomes for children in the foster care system are well studied, so one could in principal easily replicate the study comparing outcomes between children in the foster care system and those adopted into homes shared by stable same sex couples (you couldn't likely restrict it to married same sex couples, though, because laws permitting same sex civil marriage are too recent to observe outcomes).

>My bottom line-that I don’t see many disagree with if they are being intellectually honest, is a stable monogamous heterosexual family structure is the best model for immediate families. Or would you disagree?

But that's not the question at hand, is it? What we are interested in here is comparing kids bouncing around the state care system to those adopted into homes with two same-sex parents in a stable relationship.

That is exactly my point. The comparison you propose is uninformative relative to the question of permitting same sex couples to "foster to adopt". Because the counterfactual for those children is not likely to be a "stable monogamous heterosexual family". It is bouncing around the foster care system.

Some of this is based on a post I made a few days ago. By karma I don't mean a cosmic ledger of good vs. bad deeds, I mean the intrinsic metaphysical rules that govern causality. Also, the mathematical structure of causality has been studied extensively, e.g. see this and this (admittedly expensive and dense book).

I'm just putting together what I know about mathematics, computer science, biology, and metaphysical philosophy (heavily weighted towards Buddhist); in particular, I am taking a given that we are all eternal observers that have chosen to participate in one "reality" that has causality built into it. With just this assumption alone it is possible to infer a bunch of other propositions about the way things "work".

> Yeah but that tree falls in the forest if nobody's around.

Depends what you mean by "nobody". The animals in the forest are observers, so their participation in the causal "matrix" (if you will) means that the event gets "recorded". But if there are truly no observers, there is no Akashic record, so it never "happened".

Re: "aliens". They are also observers. However, they may understand the rules better than human observers do, enabling them to manipulate reality better than we do. E.g., modern engineers better understand the laws of physics than people did a hundred years ago, enabling them to create instruments that manipulate physical systems with more control than what existed a hundred years ago. Same idea. We are all still bound to the basic laws of causation.

>this is not really a good way to think about experimental design. this book goes into model development regarding controlling variables and randomizing inputs, and developing counterfactuals.

Expand on this. Why not?

Fundamentally to test in experimental conditions is to test a part of the system in isolation. This requires knowledge of what to isolate, what it does to the the overall system and confidence that all external factors have been accounted for.

Similarly, a model only conveys what we are aware of and its level of complexity is limited by what we think is sensible given available computational capability and the requirements for the model.

Looping back to my usual point, this makes such experimentation highly unreliable when applied to the economy as it is an immensely complex system.

>This course I took around 2013 also goes into what influence different graph nodes implicate on one another. So, no, scale wise, there's not a "hard limit". Not one present in this book's examples anyways.

What do you mean by "hard limit"? To what?

Logically that statement does not align. It sounds like: You took course, therefore there is no hard limit, at least according to the book.

This doesnt make sense. I can't respond to that. Expand please.

>Nope, just that "emergent order" has no boundary to tell which is fictitious and what isn't.

We know a-priori that, say, the solar system emerged and wasnt imposed by deliberate action of some conscious entity, specifically not through human action. I get what you mean, we can't prove it empirically. I argue we don't have to.

>Right, wrong, and "end up" are all functions of input by a biased humanoid. Particularly involving "errors of input", data selection, and adapting older models (knowledge) to newer.

Look, if my goal is to train an AI to identify trees, then a tree would be right, a non tree would be wrong. The fact that we call a stick with dangling bits a "tree" has little significance in this context.

>I'm currently planning and executing a FPGA Evolvable Hardware project, where "we don't know what our ignorance looks like" on the circuitry level". Doesn't default to "emergent order"; no more than a "God of the Gaps" defaults to "Must be God".

Within the framework that I have outlined emergent order is an order that occurs naturally without deliberate outside interference by means of human action.

Its been a while since I did anything with FPGAs/ASICs but you essentially run a genetic algorithm of sorts to come up with the most efficient design given initial parameters and restrictions.

Either way you clarified that you don't deny existence of complexity as a whole so lets just leave this point be.

>Quite the opposite. I'm an atheist, so I'm still asserting that any aspects of "order" are imposed. We don't have any "somewhat ordered", "non-ordered", "imposed ordered", "emergent ordered", "99.999% deterministic ordered", "43.2% non-deterministic ordered" (....etc...) Universes to compare against. Thus, no demarcation of repeatability and falsifiability. We're stuck with what we got, and any claims of "order" are likely made by someone with something to prove. Teleology ain't a science.

>The fact that you can't articulate this doesn't reveal insidiousness, rather, it reveals the knowledge you've learned is biased. It's like bad set theory by reusing "error", "loss", "noise", and other "wrong" variables in inappropriate contexts.

This looks to me like an argument of definitions with an ultra-empiricist twist.

Arguing definitions across frameworks is pointless. Definitions dont prove anything in their own right but are merely tools to assist in conveying a message.

Lets take a step back, and before we continue this discussion define "emergent order" the way you see it within your framework, then lets compare to the way I define it to see if we are discussing the same thing to begin with.

> Almost all economic data is non-experimental and thus cannot be used to demonstrate causation. We cannot say "x causes recessions".

Without commenting on the rest, I just want to note that it actually is possible to infer causation without experimentation. There's been work on this that came out of economics, out of AI research, etc, but the net effect is that we can sometimes infer causality information purely from observational data.

Example: Consider four variables, A, B, C, and D.

Now, let's say you notice, for instance, that A and B are statistically independent. From this you could reasonably conclude that there's no causal link between A and B, neither A->B, nor B->A, nor some hidden common variable influencing both of them.

However, let's say that the data is such that A and B are both statistically dependent on C (And similarly, A and B are conditionally dependent given C)

From this one could conclude either A has causal influence over C, or some common cause influences both A and C. (I'll just say "causes" to mean "has causal influence over" from now on.)

Similarly, B causes C or some hidden variable causes both B and C.

There, we've now inferred some causality information. Not with absolute certainty, but reasonably. But can we do better? So far what we've found was stuff of the form "X and Y have a causal link, and Y does not cause X"

But can we actually nail down an X causes Y link?

Enter the D.

Suppose D has the following properties: D and C are statistically dependent (and remain so even if you condition on, say, A or B or both.)

Further, D and A are statistically dependent.

Further, suppose that if you condition on C, D and A become statistically independent. (That is, if you know C, knowing A provides no further information on D)

If all of the above are true, that is, if A, B, C, and D all obey the properties listed above, then we may deduce that C causes D.

This is a relatively simple example. Judea Pearl, incidentally, has done lots of work in this area and has a book on it, named, appropriately enough, Causality. (I think some newer printings with corrections for that edition are coming.)

> causality

I've finished this book twice now and built several software models off of it; I urge you to do the same. Pay particular attention to the "do" operator and what constitutes the difference between social datasets (people, groups of populations, sampling ,social studies, economics, sociology), and what constitutes non-human biological and chemical ones. To "Do" is quite different between these contexts.

>believe in concepts like logical constructs.

Tell me how Charles Boole completely nailed Qubit spin.


Also, if I'm irrational, it means I don't divide evenly.

If you want to start with very basics I suggest http://www.cs.technion.ac.il/~dang/books/Learning%20Bayesian%20Networks%28Neapolitan,%20Richard%29.pdf

This book is also very fine to read: http://www.amazon.com/Causality-Reasoning-Inference-Judea-Pearl/dp/052189560X

and while I was searching for it I stumbled upon this article:

https://www.cs.berkeley.edu/~russell/papers/hbtnn-bn.pdf

I have no experience in your field, but here is another google hit you may find relevant: http://www.bayesialab.com/book

Concerning software, you may want to check this out: http://www.phil.cmu.edu/tetrad/publications.html again I don't have enough experience to actually answer the software question.

Here is an article about macro-level quantum phenomena in biology.

How much do you know about quantum mechanics? If very little, and if you have the mathematical and basic physical background, it's worth reading (or brushing up on) an introductory textbook, as you'll learn about (or recall) the intrinsic randomness (and observer-dependence) of the physical world at the atomic level.

Also, your OP (which was very nice, BTW) mentions a lot about causation, and it might be worth reading scientific treatments of causation. The seminal work is Judea Pearl's Causality, but it is mathematically very dense. So is the Robins school of causal inference in epidemiology (see also other stochastic process treatments of causation, in the causal inference subdiscipline of epidemiology and statistics).

Hope this helps!

I was unable to find a TOC for the book you mention, but the causality book's TOC can be seen here . I am also behind schedule because I didnt have people to discuss with, so have not reached the interventions part of the book. From what I see, causality is about the basics of graphical models, with a focus on causal models. This includes inferring what the graphs are like from the data, dealing with unobserved variables, dealing with sudden actions like fixing a variable etc. All the topics in the book you mention find their place here, but i dont know how the books compare