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#Inference #causal #reading
Building on the structural approach to causality introduced by Haavelmo (1943) and the graph-theoretic framework proposed by Pearl (1995), the artificial intelligence (AI) literature has developed a wide array of techniques for causal learning that allow leveraging information from various imperfect, heterogeneous, and biased data sources (Bareinboim and Pearl, 2016). In this paper, we discuss recent advances made in this literature that have the potential to contribute to econometric methodology along three broad dimensions. First, they provide a unified and comprehensive framework for causal inference, in which the above-mentioned problems can be addressed in full generality. Second, due to their origin in AI, they come together with sound, efficient, and complete (to be formally defined) algorithmic criteria for automatization of the corresponding identification task. And third, because of the nonparametric description of structural models that graph-theoretic approaches build on, they combine the strengths of both structural econometrics as well as the potential outcomes framework, and thus offer an effective middle ground between these two literature streams
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Flashcard 7086772981004

Tags
#causality #has-images #statistics


Question
Unfortunately, even if we only condition on [...] co- variates, we can still induce collider bias. Consider what would happen if we condition on the collider 𝑍 2 in Figure 4.16. Doing this opens up a backdoor path, along which non-causal association can flow
Answer
pretreatment

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Unfortunately, even if we only condition on pretreatment co- variates, we can still induce collider bias. Consider what would happen if we condition on the collider 𝑍 2 in Figure 4.16. Doing this opens up a backdoor path, along which non-caus

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Flashcard 7086774816012

Tags
#Data #GAN #reading #synthetic
Question
[...] is a structural element that ensures that the relationships among different sets of data are accurate. In order for synthetic data — completely artificial and private production data — to be useful, it must maintain these relationships among the data, including preserving the data links that can stretch across multiple tables.
Answer
Referential integrity

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Referential integrity is a structural element that ensures that the relationships among different sets of data are accurate. In order for synthetic data — completely artificial and private production data —

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#causality #has-images #statistics

There are two categories of things that could go wrong if we condition on descendants of 𝑇:

1. We block the flow of causation from 𝑇 to 𝑌.

2. We induce non-causal association between 𝑇 and 𝑌.

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for the backdoor adjustment (Theorem 4.2), not only did we specify that the adjustment set 𝑊 blocks all backdoor paths, but we also specified that 𝑊 does not contain any descendants of 𝑇 . Why? <span>There are two categories of things that could go wrong if we condition on descendants of 𝑇: 1. We block the flow of causation from 𝑇 to 𝑌. 2. We induce non-causal association between 𝑇 and 𝑌. <span>

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Flashcard 7086779272460

Tags
#causality #statistics
Question
The [...]-Unconfoundedness Tradeoff
Answer
Positivity

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The Positivity-Unconfoundedness Tradeoff

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