is.informative(model, query, given)

[macartan]

says which nodes are informative for a query given other nodes

for example:

A -> B -> C

A is not informative for B causes C given B

[till-tietz]

This is not yet implemented right?

[macartan]

No. This can be done using the dagitty package. The question is: * Which no nodal types are involved in a query * Which nodes are d connected to these nodal types given specified data

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On Fri, 1 Sept 2023, 11:56 Till Tietz, ***@***.***> wrote: This is not yet implemented right? — Reply to this email directly, view it on GitHub <#42 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ADBE57LQENFUR5YRBSPLH5LXYIVWDANCNFSM4HWI67MQ> . You are receiving this because you were assigned.Message ID: ***@***.***>

[till-tietz]

This seems like a somewhat trickier problem. It's basically the same question we were working on with regards to graph splitting right? That is we need to figure out how missingness and confounding affect d-separation here.

[macartan]

For a population yes but was thinking of this just for a case

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On Fri, 1 Sept 2023, 17:38 Till Tietz, ***@***.***> wrote: This seems like a somewhat trickier problem. It's basically the same question we were working on with regards to graph splitting right? That is we need to figure out how missingness and confounding affect d-separation here. — Reply to this email directly, view it on GitHub <#42 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ADBE57N2IZJM6TS4DHR5VNDXYJ52FANCNFSM4HWI67MQ> . You are receiving this because you were assigned.Message ID: ***@***.***>

[till-tietz]

I see. How would you go about figuring this out for a case?
I feel like I need to read about case vs population level queries in the book again.

[macartan]

something like this: in this version you specify which thetas you need to learn about; that's easier than having to figure them out ``` is_informative <- function(model, given_nodes, query_nodes) { statement <- model$statement |> paste(";", paste0(query_nodes, " <- t_", query_nodes, collapse = "; " )) dag <- dagitty(paste("dag{", statement, "}")) sapply(model$nodes, function(X) { dconnected(dag, X, query_nodes, given_nodes)}) } model <- make_model("X -> M -> Y") # Y is not informative about theta^X given M is_informative(model, given_nodes = list("M"), query_nodes = "X") # X is informative about theta^M given M is_informative(model, given_nodes = list("M"), query_nodes = "M") # X is not informative about theta^Y given M is_informative(model, given_nodes = list("M"), query_nodes = "Y") # is this right is_informative(model, query_nodes = c("M", "Y"), given_nodes = c("X", "Y")) ```

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On Sat, Sep 2, 2023 at 2:50 AM Till Tietz ***@***.***> wrote: I see. How would you go about figuring this out for a case? — Reply to this email directly, view it on GitHub <#42 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ADBE57PERSTCGE72XEN4EC3XYJ7DVANCNFSM4HWI67MQ> . You are receiving this because you were assigned.Message ID: ***@***.***>

[till-tietz]

Ahhh ok; this makes sense