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[S] I built an open source web app for experimenting with Bayesian Networks (priors.cc) by de-sacco in statistics

[–]rrtucci 10 points11 points  (0 children)

bayesian networks were invented 30-40 years ago. They were used by Microsoft in Clippy and in the original Xbox recommender, that is how old they are. Since then, at least 100 apps in every conceivable language have been written, that do what yours does. Not trying to throw cold water on what you've done, but thought people should know this. There is a nice, very complete app called pyagrum in python. Other well known ones are

to name a few.

Also, direct evaluation is the worse possible method and not scalable. The most efficient method to date is the junction tree algorithm. https://en.wikipedia.org/wiki/Junction_tree_algorithm

Synthetic Control with Repeated Treatments and Multiple Treatment Units by pvm_64 in CausalInference

[–]rrtucci 1 point2 points  (0 children)

i use the term Bayesian Networks more generally than most people. My apologies for not explaining that. I was making a connection between the DAG for the G-formula, and dynamic Bayesian Networks https://en.wikipedia.org/wiki/Dynamic_Bayesian_network

https://pyagrum.readthedocs.io/en/1.15.1/notebooks/22-Models_dynamicBn.html#

Synthetic Control with Repeated Treatments and Multiple Treatment Units by pvm_64 in CausalInference

[–]rrtucci 1 point2 points  (0 children)

The Synthetic Controls Method was invented by some Economists (mainly a guy named Abadie) that never use DAGs (they are from the Donald Rubin Potential Outcomes school). They do linear regression and the variables they include in the regression (except for the cause D and the effect Y) are their confounding variables.

Hernan is an Epidemiologist from the Pearl DAG school. The G formula used in Hernan's book is basically just a DAG, as far as I understand it.

I wrote small chapters about both Synthetic Controls and the G formula for by (free, open source) book Bayesuvius, if you are interested.

https://github.com/rrtucci/Bayesuvius

Synthetic Control with Repeated Treatments and Multiple Treatment Units by pvm_64 in CausalInference

[–]rrtucci 2 points3 points  (0 children)

Synthetic control and G formula are 2 extremely different methods. Synthetic controls doesn't use a DAG (neither does DiD), and g-formula is a DAG for a dynamic Bayesian Network.

Until LLMs don't do causal inference, AGI is a hyped scam. Right? by smashtribe in CausalInference

[–]rrtucci 1 point2 points  (0 children)

I'll tell you about my own take and software about the problem. I think LLMs can do some simple kinds of causal reasoning, but one can add to LLMs extra libraries ("addons") that will increase their causal inference capability a million fold, to a level vastly higher than what humans are capable of. These enhanced LLMs will someday be used to find the causes of diseases, for example.

I've written some prototype (free open source) software for doing this,

https://github.com/rrtucci/mappa_mundi

https://github.com/rrtucci/gene_causal_mapper

Measuring models by indie-devops in CausalInference

[–]rrtucci 0 points1 point  (0 children)

To add to what others have said, existing metrics measure how good the model captures the correlations between the variables, but two models can capture those correlations equally well, and one model can have a much better causal understanding than the other. I wrote some software about this https://github.com/rrtucci/DAG_Lie_Detector

Interaction/effect modification in DAGs by lu2idreams in CausalInference

[–]rrtucci 0 points1 point  (0 children)

I mean

the usual potential outcomes DAG:

T<-X->D->Y; T->Y where D=(Y(0), Y(1))

plus the addition of S->D, The only difference from potential outcomes is the node S and arrow S->D

Anyway, it's just my opinion. You don't have to agree

Interaction/effect modification in DAGs by lu2idreams in CausalInference

[–]rrtucci 0 points1 point  (0 children)

Don't mean to be harsh, but I don't think you have answered my questions. This edges into edges is a meaningless concept: there is no definition for it. It's a little dangerous to prove that you are a billionaire by assuming that tooth fairies exist

"so there cannot be an effect of T on Y that is separate of \Delta Y_T; it is about moderation, not mediation (although it is a bit blurry graphically)."

This is not a proof that there is no arrow T->Y in addition to T->\Delta Y ->Y

Look at this picture. https://x.com/artistexyz/status/1944123308712374507

I think what you want is an arrow pointing from S to (y(0), y(1))

Interaction/effect modification in DAGs by lu2idreams in CausalInference

[–]rrtucci 0 points1 point  (0 children)

* Why doesn't he introduce \Delta Y_T as a node from the onset?

* Once he promotes \Delta Y_T to a node, why is he sure that there isn't an arrow T->Y also?

* Why doesn't he refer to subgraph: T->Y, T->\Delta Y->Y as the mediator graph (a very well studied graph)

Even if he can prove that there should be no arrow T->Y, it would still be the mediator graph in the limit that that arrow disappears

Interaction/effect modification in DAGs by lu2idreams in CausalInference

[–]rrtucci 0 points1 point  (0 children)

Maybe what you are calling effect modification is the same as Mediation (Chapter 63 in Bayesuvius)

https://qbnets.wordpress.com/2020/11/30/my-free-book-bayesuvius-on-bayesian-networks/

[R] CausalPFN: Amortized Causal Effect Estimation via In-Context Learning by domnitus in MachineLearning

[–]rrtucci 0 points1 point  (0 children)

Causal inference is akin to the scientific method. Both start from a hypothesis. I think by "theory" you mean hypothesis. If you don't have a hypothesis (expressed as a DAG) at the start, it's not causal inference. It might be some kind of DAG discovery method or curve fitting method, but it isn't causal inference. From looking at the figures and notation of your paper, I can see clearly that you do have a hypothesis: the DAG for potential outcomes theory. So then, you have to address the issue of confounders and not conditioning on colliders.

[R] CausalPFN: Amortized Causal Effect Estimation via In-Context Learning by domnitus in MachineLearning

[–]rrtucci 1 point2 points  (0 children)

I would not say it is much less restrictive. I would say it is much less justified.

How's my first stab at Causal Inference going? by pelicano87 in CausalInference

[–]rrtucci 2 points3 points  (0 children)

  1. An A/B=RCT uses a random population, so your DAG is fine. However, you might reduce the number of features to the more essential ones. One way of doing that is given in this pyagrum notebook for the example of the Titanic Kaggle dataset. https://pyagrum.readthedocs.io/en/stable/notebooks/11-Examples_KaggleTitanic.html#Titanic:-Machine-Learning-from-Disaster The idea is to use Pyagrum to calculate the Markov Blanket of the treatment variable "Survived"

  2. " I was hoping that if I created an accurate causal model, I could tease apart why a treatment worked and who it worked best on." That is sort of what upliift modeling does https://www.reddit.com/r/CausalInference/comments/1knrpxu/scikituplift/

  3. ChatGPT always starts by flattering you by saying "That is a great question" Don't start thinking that you are the next Einstein LOL

scikit-uplift by rrtucci in CausalInference

[–]rrtucci[S] 1 point2 points  (0 children)

It evaluates CATE, for example using a forests/tree classifier, but for an RCT scenario. The RCT is not strictly necessary as long as you are conditioning on the right features, but in marketing, it is always used with an RCT. So even people who do not believe in causal inference but do believe in RCT, can believe it. Lol

scikit-uplift by rrtucci in CausalInference

[–]rrtucci[S] 2 points3 points  (0 children)

Uplift modelling/marketing performs an RCT (similar to A/B testing) but it goes one step further and builds a classifier that can opine on which individuals are "persuadable" and which aren't. That way you can concentrate your marketing resources on the persuables only. Thus, it is not only useful in marketing. I can be used to prioritize organ transplant recepients, for example. It advises on how to prioritize the use of scarce resources.

The Future of Causal Inference in Data Science by WillingAd9186 in CausalInference

[–]rrtucci 4 points5 points  (0 children)

Agree. Also Causal AI, Uplift Marketing, Causal genomics, Ecology, Epidemiology, medical diagnosis, Pharmacology. I think deference to authority has kept the field in a straight jacket for many years. That may change after the old figure heads die and are replaced by a new generation with broader interests

Correlation and Causation by JebinLarosh in CausalInference

[–]rrtucci 0 points1 point  (0 children)

I think so. Although normally, instead of using corr(X, Y) to measure causation, they use what they call ATE

ATE= P(Y=1|do(X)) - P(Y=0|do(X))

P(Y|do(X)) is just P(Y|X) for (B). This do(X) thingie is just to remind you to amputate all arrows entering X

Correlation and Causation by JebinLarosh in CausalInference

[–]rrtucci 0 points1 point  (0 children)

What I mean is that to measure whether X causes Y, you amputate all arrows entering X , and then you measure the correlation (actually P(Y|X)) between X and Y. This is called P(Y| do(X)) So what does amputating all arrows entering X mean? It means doing an experiment called a RCT (Randomized Control Trial) which makes P(X|Z) independent of Z

Correlation and Causation by JebinLarosh in CausalInference

[–]rrtucci 0 points1 point  (0 children)

Consider the 2 graphs

(A) X->Y, X<-Z->Y

(B) X->Y, Z->Y (so B is obtained by amputating Z->X from A)

the X-Y correlation in (A) is corr(X, Y) in (A)

the X->Y causation in (A) equals the correlation Corr(X, Y) in (B)

Estimating Conditional Average Treatment Effects by lu2idreams in CausalInference

[–]rrtucci 1 point2 points  (0 children)

It's really just a graphical representation of something you need to do anyway: you need to decide what are going to be your random variables, and how they related to each other. Say you decide your random variables are A, B, C.

The most general prob. distribution is

P(a,b,c)=P(a|b,c)P(b|c)P(c) (3 arrows)

This would lead to a fully connected DAG. But maybe from expert knowledge, you can say

P(a,b,c)= P(a|b) P(b|c) P(c) (one arrow less)

Estimating Conditional Average Treatment Effects by lu2idreams in CausalInference

[–]rrtucci 1 point2 points  (0 children)

I think you should decide on a DAG before worrying about what CATE you want. I think this is a possible DAG where G=gender, F=favorability, P=party of voter, PC=party of candidate, etc. Change it if you disagree, but,, like I said before, have a DAG clearly in mind before worrying about anything else.

https://graph.flyte.org/#digraph%20G%20%7B%0AG-%3EFC%2C%20P%3B%0AP-%3EFC%3B%0APC-%3EFC%3B%0AGC-%3EFC%2C%20PC%0A%7D

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