# on 02-Jun-2022 (Thu)

#### Flashcard 7093071777036

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#causality #statistics
Question
Association flows along all [...] paths. In causal graphs, causation flows along directed paths.
unblocked

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Association flows along all unblocked paths. In causal graphs, causation flows along directed paths.

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#### Flashcard 7093073349900

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#causality #statistics
Question
If there is a [...] path that starts at node 𝑋 and ends at node 𝑌 , then 𝑋 is an ancestor of 𝑌
directed

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If there is a directed path that starts at node 𝑋 and ends at node 𝑌 , then 𝑋 is an ancestor of 𝑌

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#### Annotation 7093074922764

 #causality #statistics Causal graphs are special in that we additionally assume that the edges have causal meaning (causal edges assumption, Assumption 3.3)

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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.

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#### Flashcard 7093076757772

Tags
#causality #statistics
Question
[...] unit-treatment value assumption (SUTVA)
stable

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stable unit-treatment value assumption (SUTVA)

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#### Flashcard 7093078592780

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#causality #statistics
Question
Conditioning on [...] of a collider also induces association in between the parents of the collider.
descendants

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Descendants of Colliders Conditioning on descendants of a collider also induces association in between the parents of the collider.

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#### Flashcard 7093080689932

Tags
#causality #statistics
Question
if there exists at least one path between 𝑋 and 𝑌 that is [...], then we say that 𝑋 and 𝑌 are d-connected.
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if there exists at least one path between 𝑋 and 𝑌 that is unblocked, then we say that 𝑋 and 𝑌 are d-connected.

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#### Flashcard 7093082524940

Tags
#causality #statistics
Question
In machine learning, we often only care about predicting the observed outcome 𝑌 , so there is no need for [...], which means machine learning does not have to deal with this fundamental problem that we must deal with in causal inference
potential outcomes

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In machine learning, we often only care about predicting the observed outcome 𝑌 , so there is no need for potential outcomes, which means machine learning does not have to deal with this fundamental problem that we must deal with in causal inference

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#### Flashcard 7093084359948

Tags
#causality #has-images #statistics

Question
We do not have exchangeability in the data because 𝑋 is a common cause of [...] and 𝑌
𝑇

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#### Flashcard 7093085932812

Tags
#causality #statistics
Question

Assumption 3.1 (Local Markov Assumption)

Given its parents in the DAG, a node 𝑋 is [...] all its non-descendants

independent of

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Assumption 3.1 (Local Markov Assumption) Given its parents in the DAG, a node 𝑋 is independent of all its non-descendants

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#### Flashcard 7093087767820

Tags
#causality #statistics
Question
SUTVA is satisfied if unit (individual) 𝑖 ’s [...] is simply a function of unit 𝑖 ’s treatment.
outcome

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SUTVA is satisfied if unit (individual) 𝑖 ’s outcome is simply a function of unit 𝑖 ’s treatment.

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#### Flashcard 7093090389260

Tags
#DAG #causal #edx #has-images
[unknown IMAGE 7092564790540]
Question
Let's start by considering two extreme examples. In the first causal graph here you see that A and Y have no common causes. And therefore, any association between them will be [...]. This is the setting that we expect to find in a randomized experiment.
causation

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Let's start by considering two extreme examples. In the first causal graph here you see that A and Y have no common causes. And therefore, any association between them will be causation. This is the setting that we expect to find in a randomized experiment.

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#### Flashcard 7093092224268

Tags
#DAG #causal #edx #has-images
[unknown IMAGE 7092578422028]
Question
In the second graph here, you see that A and Y have a [...], L. But there is no causal effect of A on Y. In this setting, all the association between A and Y is due to confounding.
common cause

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In the second graph here, you see that A and Y have a common cause, L. But there is no causal effect of A on Y. In this setting, all the association between A and Y is due to confounding.

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#### Flashcard 7093095632140

Tags
#DAG #causal #edx #has-images
[unknown IMAGE 7093096156428]
[unknown IMAGE 7093094321420]

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#### Flashcard 7093101137164

Tags
#DAG #causal #edx #has-images
[unknown IMAGE 7093101661452]
[unknown IMAGE 7093099826444]

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