Edited, memorised or added to reading list

on 27-May-2022 (Fri)

Do you want BuboFlash to help you learning these things? Click here to log in or create user.


#DAG #causality #statistics
#DAG #causality #has-images #statistics
Answer: conditional association
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

pdf

cannot see any pdfs




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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
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

Original toplevel document (pdf)

cannot see any pdfs







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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
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>

Original toplevel document (pdf)

cannot see any pdfs







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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
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

Original toplevel document (pdf)

cannot see any pdfs







#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
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
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

Original toplevel document (pdf)

cannot see any pdfs




#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
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
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>

Original toplevel document (pdf)

cannot see any pdfs




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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

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

Original toplevel document (pdf)

cannot see any pdfs







#causality #statistics
The flow of association is symmetric
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
The flow of association is symmetric, whereas the flow of causation is not.

Original toplevel document (pdf)

cannot see any pdfs




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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
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>

Original toplevel document (pdf)

cannot see any pdfs







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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

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

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 7089828269324

Tags
#DAG #causality #has-images #statistics

#DAG #causality #statistics

Question
Answer: [...]
Answer
conditional association

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Answer: conditional association

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 7089830104332

Tags
#DAG #causality #has-images #statistics


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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Example of not blocked the path A - Y

Original toplevel document (pdf)

cannot see any pdfs







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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
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>

Original toplevel document (pdf)

cannot see any pdfs







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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

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

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 7089999449356

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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

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

Original toplevel document (pdf)

cannot see any pdfs







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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

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

Original toplevel document (pdf)

cannot see any pdfs







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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
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) .

Original toplevel document (pdf)

cannot see any pdfs







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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

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

Original toplevel document (pdf)

cannot see any pdfs







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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
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.

Original toplevel document (pdf)

cannot see any pdfs







#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 .”

statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it

Original toplevel document (pdf)

cannot see any pdfs




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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

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

Original toplevel document (pdf)

cannot see any pdfs