# on 08-May-2022 (Sun)

#### Annotation 7082264366348

 #causality #has-images #statistics We refer to the flow of association along directed paths as causal association

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We refer to the flow of association along directed paths as causal association. A common type of non-causal association that makes total association not causation is confounding association. In the graph in Figure 3.20, we depict the confounding association in red

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#### Annotation 7082266201356

 #causality #statistics Causal graphs are special in that we additionally assume that the edges have causal meaning (causal edges assumption, Assumption 3.3). This assumption is what introduces causality into our models, and it makes one type of path take on a whole new meaning: directed paths.

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Causal graphs are special in that we additionally assume that the edges have causal meaning (causal edges assumption, Assumption 3.3). This assumption is what introduces causality into our models, and it makes one type of path take on a whole new meaning: directed paths. This assumption endows directed paths with the unique role of carrying causation along them. Additionally, this assumption is asymmetric; “ 𝑋 is a cause of 𝑌 ” is not the same as saying

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#### Annotation 7082268036364

 #causality #statistics The Positivity-Unconfoundedness Tradeoff Although conditioning on more covariates could lead to a higher chance of satisfying unconfoundedness, it can lead to a higher chance of violating positivity.

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The Positivity-Unconfoundedness Tradeoff Although conditioning on more covariates could lead to a higher chance of satisfying unconfoundedness, it can lead to a higher chance of violating positivity. As we increase the dimension of the covariates, we make the subgroups for any level 𝑥 of the covariates smaller.

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#### Flashcard 7082270395660

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#causality #statistics
Question
As we discussed in Section 4.2, the graph for the interventional distribution 𝑃(𝑌 | do(𝑡)) is the same as the graph for [...] distribution 𝑃(𝑌, 𝑇, 𝑋) , but with the incoming edges to 𝑇 removed.
the observational

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As we discussed in Section 4.2, the graph for the interventional distribution 𝑃(𝑌 | do(𝑡)) is the same as the graph for the observational distribution 𝑃(𝑌, 𝑇, 𝑋) , but with the incoming edges to 𝑇 removed.

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#### Annotation 7082272230668

 #causality #statistics The graph with edges removed is known as the manipulated graph

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ened node has no causal parents is that the intervened node is set to a constant value, so it no longer depends on any of the variables it depends on in the observational setting (its parents). <span>The graph with edges removed is known as the manipulated graph <span>

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#### Flashcard 7082274327820

Tags
#causality #has-images #statistics

Question
This non-causal [...] flows along the 𝑇 ← 𝑋 → 𝑌 path
association

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This non-causal association flows along the 𝑇 ← 𝑋 → 𝑌 path

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#### Annotation 7082276162828

 #causality #statistics Whenever, do(𝑡) appears after the conditioning bar, it means that everything in that expression is in the post-intervention world where the intervention do(𝑡) occurs.