numpyro.render_model render unexpected graph with handler.do

[yayami3]

Similar to #1710.
The reproduced code is as follows.

import numpyro
import numpyro.distributions as dist

from numpyro.handlers import do

def model(y=None):
    alpha = numpyro.sample("alpha", dist.Normal(0., 1.))
    beta = numpyro.sample("beta", dist.Normal(alpha, 1.))
    gamma = numpyro.sample("gamma", dist.HalfNormal(beta))

    return gamma
    
intervention = {
    "beta": 0.,
}
model_intervention = do(model, intervention)

numpyro.render_model(model_intervention)

This code render the graph below.

But expected is the edge between alpha and beta is cut and the edge between beta and gamma exists.

[fehiepsi]

Yeah, this is an issue. Just curious: will there be a link from alpha to beta? The log density of beta depends on alpha, so I guess there will be a link (like the links from latent variables to the observations). But I'm not sure if there is an interpretation of do that breaks such a link.

[yayami3]

I think it does not make sense to consider the log density of beta under do.
beta behaves like a constant. I do not understand in detail how numpyro handles variables under do though.

[fehiepsi]

I guess it implies that beta is no longer a random variable, so using grey circle might not be correct. Do you have a reference for a graphical model with intervention?

[yayami3]

@fehiepsi
Absolutely! But I think there seems to be no established way to describe it.
If you look at this journal Pearl represents the nodes as dots, not sure about the coloring.

I found pymc example. They describe intervened variables using grey circle with assigned value.