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DAG stands for Directed Acyclic Graph. DAGs are commonly used to help people think about causal patterns amongst variables.

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What justifies adjusting for proxy variables in the DAG causal inference framework?

Since Interest_in_Acme is unobservable, the average causal effect of Loyalty Club Membership on Spend is unidentifiable. However, there is an important exception to that rule, that is if Interest_in_A …
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5 votes

Meaning of multiple exposures in a DAG

DAG 1: In the first case, you rely on a very special case where the average causal effect (ACE) can be estimated. … The $C \rightarrow E1$ in the second DAG doesn't mean much. In our intervention (on both exposures), we are setting both $E1$ and $C$. …
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4 votes
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Blocking on descendants in a givenDAG

There are two valid sets to close the backdoor paths $\{Z_4, Z_1\}$ and $\{Z_2, Z_1\}$ To understand why, I will walk through my thought process. First, $Z_1$ requires being conditioned on, since it …
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1 vote

How to understand missing data mechanisms using DAGs

we can also replace the direct $Y \rightarrow R_Y$ instead with a unobserved variable $U$ linking $Y$ and $R_Y$ I highly recommend the below source for the application of missing data in the causal DAG
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0 votes

Positivity assumption and Conditional exchangeability

So, I take the perspective that we should phrase the assumptions like, "exchangeability with positivity". My reasoning follows a similar line to what you state above: exchangeability is only well-defi …
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