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Flashcard 7092440796428

Answer
The term β€œfermentation” is derived from the Latin verb fervere, to boil, thus describ- ing the appearance of the action of yeast on the extracts of fruit or malted grain. The boiling appearance is due to the production of carbon dioxide bubbles caused by the anaerobic catabolism of the sugar present in the extract.

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#DAG #causal #edx

What is the backdoor path criterion?

This is a graphical rule that tells us whether we can identify the causal effect of interest if we know the causal DAG. And the rule is the following: we can identify the causal effect of A and Y if we have sufficient data to block all backdoor paths between A and Y. We sometimes refer to these variables that we use to eliminate the backdoor path as confounders.

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Flashcard 7093022756108

Question
[default - edit me]
Answer
Release free nitrate ions which are converted to NO β€”> metabolized by glutathione S- transferase

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Flashcard 7093030620428

Tags
#causality #statistics
Question

Assumption 3.3 ((Strict) Causal Edges Assumption)

In a directed graph, every parent is a [...] of all its children

Answer
direct cause

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Assumption 3.3 ((Strict) Causal Edges Assumption) In a directed graph, every parent is a direct cause of all its children

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Flashcard 7093032717580

Tags
#causality #statistics
Question
An estimand is the quantity that we want to [...].
Answer
estimate

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An estimand is the quantity that we want to estimate.

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Flashcard 7093034814732

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#causality #statistics
Question

Assumptions of causal inference:

1. Unconfoundedness (Assumption 2.2)

2. Positivity (Assumption 2.3)

3. [...] (Assumption 2.4)

4. Consistency (Assumption 2.5)

Answer
No interference

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Assumptions of causal inference: 1. Unconfoundedness (Assumption 2.2) 2. Positivity (Assumption 2.3) 3. No interference (Assumption 2.4) 4. Consistency (Assumption 2.5)

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Flashcard 7093036649740

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#causality #statistics
Question

The Positivity-Unconfoundedness Tradeoff

Although conditioning on more covariates could lead to a higher chance of satisfying [...], it can lead to a higher chance of violating positivity.

Answer
unconfoundedness

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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.

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Flashcard 7093038484748

Tags
#causality #statistics
Question
More generally, the potential outcome π‘Œ(𝑑) denotes what your outcome would be, if you were to take treatment 𝑑 . A potential outcome π‘Œ(𝑑) is distinct from the observed outcome π‘Œ in that not all potential outcomes are observed. Rather all potential outcomes can [...] be observed. The one that is actually observed depends on the value that the treatment 𝑇 takes on
Answer
potentially

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e would be, if you were to take treatment 𝑑 . A potential outcome π‘Œ(𝑑) is distinct from the observed outcome π‘Œ in that not all potential outcomes are observed. Rather all potential outcomes can <span>potentially be observed. The one that is actually observed depends on the value that the treatment 𝑇 takes on <span>

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Flashcard 7093040581900

Tags
#causality #has-images #statistics


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

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

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Flashcard 7093042679052

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#causality #statistics
Question
More generally, the [...] π‘Œ(𝑑) denotes what your outcome would be, if you were to take treatment 𝑑
Answer
potential outcome

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More generally, the potential outcome π‘Œ(𝑑) denotes what your outcome would be, if you were to take treatment 𝑑

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Flashcard 7093044776204

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#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, [...] association between them will be causation. This is the setting that we expect to find in a randomized experiment.
Answer
any

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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 7093046611212

Tags
#DAG #causal #edx
Question
Today we will focus on confounding in a setting with no selection bias, with no measurement error, and with such a large population that we do not need to worry about [...]
Answer
chance variability

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Today we will focus on confounding in a setting with no selection bias, with no measurement error, and with such a large population that we do not need to worry about chance variability

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[unknown IMAGE 7092578422028] #DAG #causal #edx #has-images
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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usal 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. <span>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. <span>

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Flashcard 7093051067660

Tags
#DAG #causal #edx
Question

What is the [...]?

This is a graphical rule that tells us whether we can identify the causal effect of interest if we know the causal DAG. And the rule is the following: we can identify the causal effect of A and Y if we have sufficient data to block all backdoor paths between A and Y. We sometimes refer to these variables that we use to eliminate the backdoor path as confounders.

Answer
backdoor path criterion

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What is the backdoor path criterion? This is a graphical rule that tells us whether we can identify the causal effect of interest if we know the causal DAG. And the rule is the following: we can identify the causal effect

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Flashcard 7093052902668

Tags
#DAG #causal #edx
Question

Two sources of bias:

- [...] (confounding)

- conditioning on common effect (selection bias)

Answer
common cause

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Two sources of bias: - common cause (confounding) - conditioning on common effect (selection bias)

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Flashcard 7093054737676

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#DAG #causal #edx
Question
We have seen that [...] is a systematic bias when we are conducting causal inference research.
Answer
confounding

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We have seen that confounding is a systematic bias when we are conducting causal inference research.

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Flashcard 7093056572684

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#DAG #causal #edx
Question
When there is an association between A and Y, even if A has a [...] causal effect, a zero causal effect on Y, then we say that there is bias under the null.
Answer
null

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When there is an association between A and Y, even if A has a null causal effect, a zero causal effect on Y, then we say that there is bias under the null.

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Flashcard 7093058145548

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#DAG #causal #edx
Question
When there is [...] between A and Y, even if A has a null causal effect, a zero causal effect on Y, then we say that there is bias under the null.
Answer
an association

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When there is an association between A and Y, even if A has a null causal effect, a zero causal effect on Y, then we say that there is bias under the null.

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Flashcard 7093059980556

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#DAG #causal #edx
Question

Other (wrong definitions of confounder):

- [...] definition

- conventional definition

Answer
change in estimate

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Other (wrong definitions of confounder): - change in estimate definition - conventional definition

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Flashcard 7093061815564

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#DAG #causal #edx
Question
[...] is an association between the treatment A and the outcome Y that does not arise from the causal effect of A on Y.
Answer
Systematic bias

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Systematic bias is an association between the treatment A and the outcome Y that does not arise from the causal effect of A on Y.

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