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Questions and Answers
In the context of Directed Acyclic Graphs (DAG), what is the primary purpose of controlling for confounders?
Which type of paths in a DAG are considered non-causal?
What are back-door paths in a DAG associated with?
How are causal paths identified in a Directed Acyclic Graph (DAG)?
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What happens to the identification of a causal effect when there are open non-causal paths in a DAG?
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In a Directed Acyclic Graph (DAG), what does X1 represent in the context of the causal relationship between treatment D and outcome Y?
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What is the main concern related to back-door paths in establishing causal effects between treatment D and outcome Y in a DAG?
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How can a complex path like D → X3 ← X4 → X5 → Y be decomposed into simpler structures in a DAG analysis?
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Which type of variable is X2 in relation to the causal path between D and Y?
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What is the primary role of Directed Acyclic Graphs (DAGs) in identifying causal effects?
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What is the role of randomization in closing back-door paths in a Directed Acyclic Graph (DAG)?
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In a DAG, what does conditioning on a common outcome (collider) typically do to the relationship between the causes?
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Which criterion states the conditions under which back-door paths are blocked in a Directed Acyclic Graph (DAG)?
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If a back-door path contains a fork I ← M → J in a DAG, when is this path considered blocked?
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What happens if an observed variable X2 is omitted in a Directed Acyclic Graph (DAG) when trying to close back-door paths?
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In a Directed Acyclic Graph (DAG), what do the nodes represent?
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What does an acyclic path in a DAG signify?
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How is omitted variable bias related to Directed Acyclic Graphs (DAGs)?
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What is the purpose of closing open non-causal paths in Directed Acyclic Graphs?
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What happens if there are open non-causal paths in a Directed Acyclic Graph (DAG)?
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In a Directed Acyclic Graph (DAG), how is a back-door path closed?
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How is the causal effect of D on Y identified in a DAG when there are two back-door paths?
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What happens when X1 and X2 are both conditioned on in a DAG with a non-causal path D → X1 ← X2 → Y?
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Why is X1 considered a bad control in certain cases according to DAG analysis?
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How does regression of Y on D and X1 help in identifying causal effects according to DAG analysis?
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What is the purpose of a directed acyclic graph (DAG) in causal inference?
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How does omitted variable bias impact the estimation of causal effects?
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Why is identifying back-door paths important in causal inference?
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In terms of causal effect identification, what does the counterfactual Y0 represent?
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How does the concept of indirect effects relate to causal inference?
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What is the main assumption about Directed Acyclic Graphs (DAGs) mentioned in the text?
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What distinguishes the under-intervention regime from the pre-intervention regime in causal analysis?
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Which probability distributions define the causal effects in Pearl’s variant of causal analysis?
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What does AT E represent in the context of causal analysis?
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How does the do(·) operator impact causal analysis in relation to Directed Acyclic Graphs (DAGs)?
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What is the significance of under-intervention distributions in defining causal quantities?
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What is the primary role of potential outcomes in Pearl’s variant of causal analysis?
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'Back-door paths' in a Directed Acyclic Graph (DAG) are typically associated with:
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'Omitted Variable Bias' refers to:
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'Indirect effects' in a Directed Acyclic Graph (DAG) refer to:
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What is the purpose of identifying and closing back-door paths in the context of causal inference?
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How does the concept of counterfactuals relate to causal inference in Directed Acyclic Graphs (DAGs)?
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What is the main concern when there are open non-causal paths in a Directed Acyclic Graph (DAG)?
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Why is X1 considered a bad control in certain cases according to DAG analysis?
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In terms of causal effect identification, what does the counterfactual Y0 represent?
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What is the purpose of identifying and closing back-door paths in a Directed Acyclic Graph (DAG)?
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Explain the significance of controlling for confounders in causal inference using DAGs.
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What is the role of counterfactuals and potential outcomes in determining causal effects?
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How does the concept of average treatment effect (ATE) contribute to understanding causal relationships?
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What is the observational problem related to causal inference, and how does it impact establishing causal effects?
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Explain the difference between the observed outcome and the potential outcome in the context of counterfactuals and potential outcomes.
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Define the Average Treatment Effect (ATE) and explain its significance in causal analysis.
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What is the selection effect in the context of individual causal effects, and how does it impact the interpretation of treatment effects?
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Explain why random assignment is crucial in addressing selection bias when estimating treatment effects.
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How does the counterfactual Y0 represent the unobserved potential outcome in causal analysis?
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Explain how causal graphs help in illustrating and analyzing causal models.
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Define the term 'path' in the context of Directed Acyclic Graphs (DAG).
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How can open non-causal paths in a DAG impact the identification of causal effects?
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What is the significance of controlling for confounders in a Directed Acyclic Graph (DAG)?
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Explain the importance of closing open non-causal paths in Directed Acyclic Graphs (DAGs).
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What happens when we condition on both X1 and X2 in a Directed Acyclic Graph (DAG) with a non-causal path D → X1 ← X2 → Y?
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How is the causal effect of D on Y identified in a DAG when there are two back-door paths?
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Why is X1 considered a bad control in certain cases according to DAG analysis?
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What happens when we condition on X1 in a Directed Acyclic Graph (DAG) with a back-door path D → X1 ← X2 → Y?
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How does the identification of the direct effect of D on Y differ in a DAG when X1 is a collider on the path D → X1 ← X2 → Y?
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Explain the concept of back-door paths in Directed Acyclic Graphs (DAGs) and how they are blocked.
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Define the Average Treatment Effect (ATE) and explain how it is estimated in causal analysis.
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Describe the Selection effect in the context of causal analysis and its impact on estimating causal effects.
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Explain the concept of Counterfactuals and Potential Outcomes in causal analysis.
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Discuss the importance of identifying and estimating the Causal effect in observational studies.
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What is the significance of counterfactuals in causal analysis?
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How does the concept of potential outcomes contribute to causal inference?
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What does the term 'Selection effect' refer to in causal analysis?
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Define Average Treatment Effect (ATE) in the context of causal analysis.
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How does observational problem hinder causal inference?
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Explain the term 'Causal effect' in the context of causal analysis.
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How is the identification of causal effects enhanced through randomized experiments?
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What role does matching play in causal analysis?
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Explain the concept of instrumental variables in establishing causal effects.
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How does regression discontinuity aid in causal inference?
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