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Flashcard 7092440796428

Answer

The term “fermentation” 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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Annotation 7092588907788

#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 —> 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

Tags

#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

Tags

#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 𝑌(𝑡) denotes what your outcome would be, if you were to take treatment 𝑡 . A potential outcome 𝑌(𝑡) is distinct from the observed outcome 𝑌 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 𝑡 . A potential outcome 𝑌(𝑡) is distinct from the observed outcome 𝑌 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

Tags

#causality #statistics

Question

More generally, the [...] 𝑌(𝑡) denotes what your outcome would be, if you were to take treatment 𝑡

Answer

potential outcome

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More generally, the potential outcome 𝑌(𝑡) denotes what your outcome would be, if you were to take treatment 𝑡

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Flashcard 7093044776204

Tags

#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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Annotation 7093048446220

[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 [...]?

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

Tags

#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

Tags

#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

Tags

#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

Tags

#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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on 01-Jun-2022 (Wed)

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