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# Questions tagged [causal-diagrams]

A causal diagram is a directed graph that displays causal relationships between variables in a causal model.

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### Mediators in Causal Diagrams

I've been reading Judea Pearl's The Book of Why to try to understand how to use causal diagrams. On page 113, Pearl gives three events, A, B, and C, where A causes B which causes C. This is displayed ...
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### Causal Graph Generation from Joint Probability Distribution and Conditional Probability

I am trying to find a causal graph from this data. ...
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### Clarification on the properties of double summation in causal inference

Below is the proof along with the causal graph I copied from a textbook about causal inference by Brady Neal: Claim Given the causal graph is Figure A.1, $P(m \mid d o(t))=P(m \mid t)$. START OF THE ...
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### In a Bayesian network, does the removal of an edge ever remove existing conditional independences?

I am wondering if the removal of any edge in a acyclic Bayesian network ever removes an existing conditional independence? Intuitively, I would think not, but I was wondering if there is a formal ...
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### How to prove d-separation implies conditional independence?

All of the materials I see online just state it as fact. I don't see it as obvious at all. I use this definition of a Belief network. And this is the definition of d-seperation from the textbook:
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### On the Derivation of Judea Pearl's Front-Door Adjustment Formula in The Book of Why

I have a number of related questions about the derivation of the front-door adjustment formula as given on page 236. Here is the derivation. I would have typed it up, but the diagrams at the far right ...
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### Conditional Independence Relations for $X_1\leftarrow X\rightarrow X_2$

Let $X$ be a random variable, and let $X_1:=g_1(X)$ and $X_2:=g_2(X)$. Does it hold that $X\perp \!\!\! \perp X_1 | (X_1, X_2)$? (This statement is made in the proof of Proposition 1 in the appendix ...
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### conditioning on the source or target variables in d-separation?

In Pearl's Causality - Models, Reasoning and Inference (2009), he defines d-separation as follows: Let $X\perp\!\!\!\perp Y |Z$ mean "$Z$ d-separates $X$ from $Y$". But there seems to be a weird ...
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### If we have two non-zero-correlated random variables, then why do we say that "correlation does not imply causation"?

If we have two non-zero correlated random variables then they are dependent. Why then do we have the saying "Correlation does not imply Causation". A change in one variable may not cause exactly the ...
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