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Annotation 7082604629260


#DAG #causality #statistics
 #DAG #causality #has-images #statistics
Answer: conditional association
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Flashcard 7088695545100

Tags
#causality #statistics
Question
In contrast, the [...(how strong?)] causal edges assumption would allow for some parents to not be causes of their children. It would just assume that children are not causes of their parents. This allows us to draw graphs with extra edges to make fewer assumptions, just like we would in Bayesian networks, where more edges means fewer independence assumptions.
Answer
non-strict

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In contrast, the non-strict causal edges assumption would allow for some parents to not be causes of their children. It would just assume that children are not causes of their parents. This allows us to draw graph

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Flashcard 7089808346380

Tags
#causality #has-images #statistics


Question
In defining the backdoor criterion (Definition 4.1) for the backdoor adjustment (Theorem 4.2), not only did we specify that the adjustment set π‘Š blocks all backdoor paths, but we also specified that π‘Š does not contain any descendants of [...]
Answer
𝑇

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ion 4.1) for the backdoor adjustment (Theorem 4.2), not only did we specify that the adjustment set π‘Š blocks all backdoor paths, but we also specified that π‘Š does not contain any descendants of <span>𝑇 <span>

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Flashcard 7089810443532

Tags
#causality #statistics
Question
As important as the local Markov assumption is, it only gives us information about the independencies in 𝑃 that a DAG implies. It does not even tell us that if 𝑋 and π‘Œ are adjacent in the DAG, then 𝑋 and π‘Œ are [...]. And this additional information is very commonly assumed in causal DAGs. To get this guaranteed dependence between adjacent nodes, we will generally assume a slightly stronger assumption than the local Markov assumption: minimality
Answer
dependent

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the local Markov assumption is, it only gives us information about the independencies in 𝑃 that a DAG implies. It does not even tell us that if 𝑋 and π‘Œ are adjacent in the DAG, then 𝑋 and π‘Œ are <span>dependent. And this additional information is very commonly assumed in causal DAGs. To get this guaranteed dependence between adjacent nodes, we will generally assume a slightly stronger assumpti

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Annotation 7089812278540

#causality #statistics
That’s the math for why we need the positivity assumption, but what’s the intuition? Well, if we have a positivity violation, that means that within some subgroup of the data, everyone always receives treatment or everyone always receives the control. It wouldn’t make sense to be able to estimate a causal effect of treatment vs. control in that subgroup since we see only treatment or only control. We never see the alternative in that subgroup
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An β€œestimator” is a function that takes a dataset as input and outputs an estimate. We discuss this statistics terminology more in Section 2.4. That’s the math for why we need the positivity assumption, but what’s the intuition? Well, if we have a positivity violation, that means that within some subgroup of the data, everyone always receives treatment or everyone always receives the control. It wouldn’t make sense to be able to estimate a causal effect of treatment vs. control in that subgroup since we see only treatment or only control. We never see the alternative in that subgroup

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Annotation 7089813851404

#causality #statistics
The potential outcome that is observed is sometimes referred to as a factual. Note that there are no counterfactuals or factuals until the outcome is observed. Before that, there are only potential outcomes
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red to as β€œcounterfactual outcomes,” but we will never do that in this book because a potential outcome π‘Œ(𝑑) does not become counter to fact until another potential outcome π‘Œ(𝑑 0 ) is observed. <span>The potential outcome that is observed is sometimes referred to as a factual. Note that there are no counterfactuals or factuals until the outcome is observed. Before that, there are only potential outcomes <span>

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Flashcard 7089815686412

Tags
#causality #statistics
Question
Consistency is the assumption that the outcome we observe π‘Œ is actually the potential outcome under the [...] treatment 𝑇
Answer
observed

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Consistency is the assumption that the outcome we observe π‘Œ is actually the potential outcome under the observed treatment 𝑇

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Annotation 7089817783564

#causality #statistics
The flow of association is symmetric
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The flow of association is symmetric, whereas the flow of causation is not.

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Flashcard 7089819618572

Tags
#causality #statistics
Question
To identify a causal effect is to reduce a causal expression to a purely statistical expression. In this chapter, that means to reduce an expression from one that uses potential outcome notation to one that uses only statistical notation such as 𝑇 , 𝑋 , π‘Œ , expectations, and conditioning. This means that we can calculate the causal effect from just the [...] distribution 𝑃(𝑋, 𝑇, π‘Œ)
Answer
observational

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t uses potential outcome notation to one that uses only statistical notation such as 𝑇 , 𝑋 , π‘Œ , expectations, and conditioning. This means that we can calculate the causal effect from just the <span>observational distribution 𝑃(𝑋, 𝑇, π‘Œ) <span>

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Flashcard 7089821453580

Tags
#causality #statistics
Question
When we say β€œestimation,” we are referring to the process of moving from a statistical estimand to [...]
Answer
an estimate

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When we say β€œestimation,” we are referring to the process of moving from a statistical estimand to an estimate

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Flashcard 7089828269324

Tags
#DAG #causality #has-images #statistics

#DAG #causality #statistics

Question
Answer: [...]
Answer
conditional association

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Answer: conditional association

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Flashcard 7089830104332

Tags
#DAG #causality #has-images #statistics


Question
Example of [...(block?)] the path A - Y
Answer
not blocked

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Example of not blocked the path A - Y

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Flashcard 7089995779340

Tags
#causality #statistics
Question

the main assumptions that we need for our causal graphical models to tell us how association and causation flow between variables are the following two:

1. Local Markov Assumption (Assumption 3.1)

2. [...] Assumption (Assumption 3.3)

Answer
Causal Edges

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in assumptions that we need for our causal graphical models to tell us how association and causation flow between variables are the following two: 1. Local Markov Assumption (Assumption 3.1) 2. <span>Causal Edges Assumption (Assumption 3.3) <span>

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Flashcard 7089997614348

Tags
#causality #statistics
Question
Conditional exchangeability is the main assumption necessary for causal inference. Armed with this assumption, we can [...] the causal effect within levels of 𝑋
Answer
identify

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Conditional exchangeability is the main assumption necessary for causal inference. Armed with this assumption, we can identify the causal effect within levels of 𝑋

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Flashcard 7089999449356

Tags
#causality #statistics
Question
An β€œestimator” is a function that takes a [...] as input and outputs an estimate.
Answer
dataset

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An β€œestimator” is a function that takes a dataset as input and outputs an estimate.

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Flashcard 7090048994572

Tags
#causality #statistics
Question
A potential outcome π‘Œ(𝑑) is distinct from the observed outcome π‘Œ in that not all potential outcomes are [...]
Answer
observed

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A potential outcome π‘Œ(𝑑) is distinct from the observed outcome π‘Œ in that not all potential outcomes are observed

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Flashcard 7090109549836

Tags
#causality #statistics
Question

the fundamental problem of causal inference

It is fundamental because if we cannot observe both π‘Œ 𝑖 (1) and π‘Œ 𝑖 (0) , then we cannot observe the [...] π‘Œ 𝑖 (1) βˆ’ π‘Œ 𝑖 (0) .

Answer
causal effect

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the fundamental problem of causal inference It is fundamental because if we cannot observe both π‘Œ 𝑖 (1) and π‘Œ 𝑖 (0) , then we cannot observe the causal effect π‘Œ 𝑖 (1) βˆ’ π‘Œ 𝑖 (0) .

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Flashcard 7090186095884

Tags
#causality #statistics
Question
Positivity is the condition that all subgroups of the data with different [...] have some probability of receiving any value of treatment
Answer
covariates

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Positivity is the condition that all subgroups of the data with different covariates have some probability of receiving any value of treatment

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Flashcard 7090273914124

Tags
#causality #has-images #statistics


Question
This is known as M-bias due to the M shape that this [...] flows along when the graph is drawn with children below their parents.
Answer
non-causal association

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This is known as M-bias due to the M shape that this non-causal association flows along when the graph is drawn with children below their parents.

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Annotation 7090608672012

#causality #statistics

causal edges assumption, endows directed paths with the unique role of carrying causation along them.

Additionally, causal edges assumption is asymmetric; β€œ 𝑋 is a cause of π‘Œ ” is not the same as saying β€œ π‘Œ is a cause of 𝑋 .”
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Flashcard 7090618633484

Tags
#causality #has-images #statistics


Question
For example, if we remove the 𝐴 β†’ 𝐡 to get Figure 3.5, then 𝐴 β†’ 𝐢 ← 𝐡 is [...]
Answer
an immorality

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For example, if we remove the 𝐴 β†’ 𝐡 to get Figure 3.5, then 𝐴 β†’ 𝐢 ← 𝐡 is an immorality

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