# on 01-Jul-2022 (Fri)

#### Flashcard 7101870378252

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#causality #statistics
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In observational data, it is unrealistic to assume that the treatment groups are exchangeable. In other words, there is no reason to expect that the groups are the same in all relevant variables other than the [...].
treatment

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data, it is unrealistic to assume that the treatment groups are exchangeable. In other words, there is no reason to expect that the groups are the same in all relevant variables other than the <span>treatment. <span>

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#### Flashcard 7101872475404

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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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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 7101874048268

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What do we do when we go to actually estimate quantities such as πΌ π [ πΌ[π | π = 1, π] β πΌ[π | π = 0, π] ] ? We will often use a model (e.g. linear regression or some more fancy predictor from machine learning) in place of the [...] πΌ[π | π = π‘, π = π₯] . We will refer to estimators that use models like this as model-assisted estimators. Now that weβve gotten some of this terminology out of the way, we can proceed to an example of estimating the ATE
conditional expectations

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lly estimate quantities such as πΌ π [ πΌ[π | π = 1, π] β πΌ[π | π = 0, π] ] ? We will often use a model (e.g. linear regression or some more fancy predictor from machine learning) in place of the <span>conditional expectations πΌ[π | π = π‘, π = π₯] . We will refer to estimators that use models like this as model-assisted estimators. Now that weβve gotten some of this terminology out of the way, we can proceed t

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#### Flashcard 7101875883276

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#causality #has-images #statistics

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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 [...]. In contrast, the minimality assumption does not allow this additional independence
independent

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cal 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 <span>independent. In contrast, the minimality assumption does not allow this additional independence <span>

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#### Flashcard 7101877718284

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The causal graph for interventional distributions is simply the same graph that was used for the observational [...] distribution, but with all of the edges to the intervened node(s) removed.
joint

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The causal graph for interventional distributions is simply the same graph that was used for the observational joint distribution, but with all of the edges to the intervened node(s) removed.

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#### Flashcard 7101880077580

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Rather, my outcome is only a function of [...]. Weβve been using this assumption implicitly throughout this chapter. Weβll now formalize it.

Assumption 2.4 (No Interference) π π (π‘ 1 , . . . , π‘ πβ1 , π‘ π , π‘ π+1 , . . . , π‘ π ) = π π (π‘ π )

my own treatment

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Rather, my outcome is only a function of my own treatment. Weβve been using this assumption implicitly throughout this chapter. Weβll now formalize it. Assumption 2.4 (No Interference) π π (π‘ 1 , . . . , π‘ πβ1 , π‘ π , π‘ π+1 , . . . , π‘ π ) = π

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#### Annotation 7101881912588

 #GAN #data #sequential #synthetic In sequential data, information can be spread through many rows, like credit card transactions, and preservation of correlations between rows β the events β and columns β the variables is key

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Generating synthetic time-series and sequential data is more challenging than tabular data where normally all the information regarding one individual is stored in a single row. In sequential data, information can be spread through many rows, like credit card transactions, and preservation of correlations between rows β the events β and columns β the variables is key. Furthermore, the length of the sequences is variable; some cases may comprise just a few transactions while others may have thousands.

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#### Flashcard 7101883747596

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When we say βidentificationβ in this book, we are referring to the process of moving from a causal estimand to an equivalent [...] estimand
statistical

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When we say βidentificationβ in this book, we are referring to the process of moving from a causal estimand to an equivalent statistical estimand

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#### Flashcard 7101885582604

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We refer to the flow of [...] along directed paths as causal association
association

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We refer to the flow of association along directed paths as causal association

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#### Flashcard 7101888466188

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Definition 3.4 (d-separation) Two (sets of) nodes π and π are d-separated by a [...] π if all of the paths between (any node in) π and (any node in) π are blocked by π

Source: Pearl (1988), Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

set of nodes

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Definition 3.4 (d-separation) Two (sets of) nodes π and π are d-separated by a set of nodes π if all of the paths between (any node in) π and (any node in) π are blocked by π Source: Pearl (1988), Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

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#### Flashcard 7101892136204

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Regular Bayesian networks are purely statistical models, so we can only talk about the flow of [...] in Bayesian networks.
association

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Regular Bayesian networks are purely statistical models, so we can only talk about the flow of association in Bayesian networks.

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#### Flashcard 7101894757644

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

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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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#### Annotation 7101905767692

 earch and review in SuperMemo is a review of a subset of elements that contain a given search phrase. For example, before an exam in microbiology, a student may wish to review all his knowledge of viruses using the following method: