# on 28-Apr-2022 (Thu)

#### Flashcard 4009107524876

Question
In terraform, declare variable "cidrs" in your *.tf file, assuming its value will be a list
variable "cidrs" {
default = []
}

OR:

variable "cidrs" {
type = list
}

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#### Annotation 7070768041228

 #causality #statistics As important as the local Markov assumption is, it only gives us information about the independencies in π that a DAG implies. It does not even tell us that if π and π are adjacent in the DAG, then π and π are 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 assumption than the local Markov assumption: minimality

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#### Annotation 7071360224524

 #causality #statistics Flow of Causation The flow of association is symmetric, whereas the flow of causation is not. Under the causal edges assumption (Assumption 3.3), causation only flows in a single direction. Causation only flows along directed paths. Association flows along any path that does not contain an immorality

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#### Annotation 7071361797388

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

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#### Annotation 7077989059852

 #causality #has-images #statistics If we condition on a descendant of π that isnβt a mediator, it could unblock a path from π to π that was blocked by a collider. For example, this is the case with conditioning on π in Figure 4.13. This induces non-causal association between π and π , which biases the estimate of the causal effect

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#### Flashcard 7077991681292

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#causality #statistics
Question

Descendants of Colliders

Conditioning on descendants of a collider also induces association in between the [...] of the collider.

parents

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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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#### Annotation 7077994302732

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

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Flow of Causation The flow of association is symmetric, whereas the flow of causation is not. Under the causal edges assumption (Assumption 3.3), causation only flows in a single direction. Causation only flows along directed paths. Association flows along any path that does not

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#### Flashcard 7077995875596

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#causality #statistics
Question

Flow of Causation

The flow of association is symmetric, whereas the flow of causation is not. Under the causal edges assumption (Assumption 3.3), causation only flows in a single direction. Causation only flows along [...] paths. Association flows along any path that does not contain an immorality

directed

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low of association is symmetric, whereas the flow of causation is not. Under the causal edges assumption (Assumption 3.3), causation only flows in a single direction. Causation only flows along <span>directed paths. Association flows along any path that does not contain an immorality <span>

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#### Flashcard 7077997710604

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#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 π , π , π , expectations, and [...]. This means that we can calculate the causal effect from just the observational distribution π(π, π, π)
conditioning

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al 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 π , π , π , expectations, and <span>conditioning. This means that we can calculate the causal effect from just the observational distribution π(π, π, π) <span>

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#### Annotation 7077999807756

 #causality #statistics We denote by π(1) the potential outcome of happiness you would observe if you were to get a dog ( π = 1 )

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#### Flashcard 7078001642764

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Conditional exchangeability is the [...] assumption necessary for causal inference. Armed with this assumption, we can identify the causal effect within levels of π
main

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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 π

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#### Flashcard 7078003739916

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Question

the fundamental problem of causal inference

It is fundamental because if we cannot observe both π π (1) and π π (0) , then we cannot [...] the causal effect π π (1) β π π (0) .

observe

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the fundamental problem of causal inference It is fundamental because if we cannot observe both π π (1) and π π (0) , then we cannot observe the causal effect π π (1) β π π (0) .

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#### Annotation 7078006099212

 #causality #has-images #statistics if the DAG were simply two connected nodes π and π as in Figure 3.8, the local Markov assumption would tell us that we can factorize π(π₯, π¦) as π(π₯)π(π¦|π₯) , but it would also allow us to factorize π(π₯, π¦) as π(π₯)π(π¦) , meaning it allows distributions where π and π are independent. In contrast, the minimality assumption does not allow this additional independence

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#### Flashcard 7078007934220

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stable [...] value assumption (SUTVA)
unit-treatment

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stable unit-treatment value assumption (SUTVA)

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#### Flashcard 7078009769228

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Question

Assumption 3.2 (Minimality Assumption)

1. Given its parents in the DAG, a node π is independent of all its [...] (Assumption 3.1).

2. Adjacent nodes in the DAG are dependent.

non-descendants

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Assumption 3.2 (Minimality Assumption) 1. Given its parents in the DAG, a node π is independent of all its non-descendants (Assumption 3.1). 2. Adjacent nodes in the DAG are dependent.

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#### Flashcard 7078011866380

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

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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 7078013701388

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SUTVA is satisfied if unit (individual) π βs outcome is simply a function of unit π βs [...].
treatment

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SUTVA is satisfied if unit (individual) π βs outcome is simply a function of unit π βs treatment.

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#### Flashcard 7078015536396

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In machine learning, we often only care about [...] the observed outcome π , 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
predicting

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In machine learning, we often only care about predicting the observed outcome π , 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 in

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#### Annotation 7078018157836

 #causality #statistics To get this guaranteed dependence between adjacent nodes, we will generally assume a slightly stronger assumption than the local Markov assumption: minimality

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s in π that a DAG implies. It does not even tell us that if π and π are adjacent in the DAG, then π and π are dependent. And this additional information is very commonly assumed in causal DAGs. <span>To get this guaranteed dependence between adjacent nodes, we will generally assume a slightly stronger assumption than the local Markov assumption: minimality <span>

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#### Annotation 7078019992844

 #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. As we increase the dimension of the covariates, we make the subgroups for any level π₯ of the covariates smaller.

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#### Flashcard 7078021827852

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An β[...]β is a function that takes a dataset as input and outputs an estimate.
estimator

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An βestimatorβ is a function that takes a dataset as input and outputs an estimate.

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#### Flashcard 7078023662860

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Consistency is the assumption that the outcome we observe π is actually the [...] under the observed treatment π
potential outcome

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Consistency is the assumption that the outcome we observe π is actually the potential outcome under the observed treatment π

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#### Flashcard 7078125374732

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#DAG #causality #has-images #statistics

#DAG #causality #statistics

#DAG #causality #statistics

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#### Flashcard 7078130355468

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#DAG #causality #statistics

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#### Flashcard 7078135860492

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missing arrows

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#### Flashcard 7078140316940

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#DAG #causality #statistics

#DAG #causality #statistics

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#### Flashcard 7078145821964

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#DAG #causality #statistics

Question
What is the symbol of conditioning?

#DAG #causality #statistics

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#### Flashcard 7078151326988

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#### Flashcard 7078156832012

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#DAG #causality #statistics

#DAG #causality #statistics

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#### Flashcard 7078166531340

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