Talk:Causal model

message: In response to this remark I have found several definitions looking for "a causal model is" in Google-books, for example:
* "According to a formal definition, a causal model is a mathematical object that provides an interpretation and computation of causal queries about the domain [Galles & Pearl 1998]..." 
* "A causal model is composed by a set of logical formulae which expresses different kinds of relationships among entities belonging to different types..." 
* "A causal model is an advanced form of dependency model that allows modelling the various scenarios that can lead to a defined state - in our case, the winding down of a ..." 
* "A causal model is a unique model describing the mechanisms of the system..." 
Now I am not sure how this could be integrated into the article, but could be a good start. -- Mdd (talk) 21:13, 9 April 2011 (UTC)
thread_title:  Definition(s) 

message: Just did a vast expansion of this. Feedback encouraged. Particular items:
* Errors. This is a complex subject. Please flag any errors and I will fix them.
* Organization. The major source of the piece is Pearl's book. It only sort of uses the organization I ended up adopting. I'd appreciate your suggestions for how to improve that bit (and anything else, for sure).
* Clarity. This piece is pretty abstract. The book does a better job than the article of including examples as a path to clarity. For brevity, I left most of this out and shifted the focus to more abstract concerns. Are more examples needed? Where? Are Pearl's examples the ones to go with?
TIA. 
Lfstevens (talk) 14:17, 28 October 2018 (UTC)
thread_title:  Expansion 

message: The article claims causal model is falsifiable: "Causal models are falsifiable, in that if they do not match data, they must be rejected as invalid."
However, this appears very different from the definition of falsifiable: "A theory or hypothesis is falsifiable (or refutable) if it can be logically contradicted by an empirical test that can potentially be executed with existing technologies." on https://en.wikipedia.org/wiki/Falsifiable .
The latter definition makes more sense to me. If that definition is more wide accepted, should the falsifiability claim in this article be removed or corrected?  — Preceding unsigned comment added by 50.234.189.45 (talk) 21:39, 7 December 2022 (UTC)
thread_title:  Is causal model falsifiable? 

message: https://www.pymc.io/projects/examples/en/latest/causal_inference/interventional_distribution.html See where the example is given that the joint distribution defined by a specific DAG is P(x_1, x_2, x_3, x_4, x_5) := P(x_1) P(x_3|x_1) P(x_2|x_1) P(x_4|x_3, x_2) P(x_5|x_4) while the interventional distribution for x_3=1 removes all terms which would influence X_3: P(x_1, x_2, \operatorname{do}(x_3=1), x_4, x_5) = P(x_1) P(x_2|x_1) P(x_4|x_3=1, x_2) P(x_5|x_4). This seems to imply that the distribution P(x_1, x_2, \operatorname{do}(x_3=1), x_4, x_5) can be estimated from the observed data by completely filtering out all cases where x_3 \neq 1 and looking at the remaining observations which especially implies differnt normalization ...?? I cite from https://www.inference.vc/untitled/ "the conditional distribution takes the population of data points (x', y') ~ p(X, Y) and filters to the subpopulation where X = x; 2. the interventional distribution sets X = x in the generative process and simulates forward". I also guess that the toy example from https://www.inference.vc/causal-inference-2-illustrating-interventions-in-a-toy-example/ would be valuable to be added! Biggerj1 (talk) 23:14, 17 May 2024 (UTC)
thread_title:  Interventional Distribution is missing