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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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#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 π‘Œ.

2. We induce non-causal association between 𝑇 and π‘Œ.

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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 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 π‘Œ. 2. We induce non-causal association between 𝑇 and π‘Œ. <span>

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Disadvantags of survey-based CX measurement

1. Limited: The typical CX survey samples only 7 percent of a company’s customers, providing an extremely limited view of what customers experience and value. In fact, only 13 percent of the CX leaders we surveyed expressed full confidence that their CX measurement system provides a representative view of their customer base

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Predictive customer scores

The company develops analyticsβ€”often using several types of machine-learning algorithmsβ€”to understand and track what is influencing customer satisfaction and business performance, and to detect specific events in customer journeys. The algorithms generate predictive scores for each customer based on journey features. These scores allow the company to predict individual customer satisfaction and value outcomes such as revenue, loyalty, and cost to serve. More broadly, they allow CX leaders to assess the ROI for particular CX investments and directly tie CX initiatives to business outcomes

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#causality #statistics

Rather, my outcome is only a function of my own treatment. We’ve been using this assumption implicitly throughout this chapter. We’ll now formalize it.

Assumption 2.4 (No Interference) π‘Œ 𝑖 (𝑑 1 , . . . , 𝑑 π‘–βˆ’1 , 𝑑 𝑖 , 𝑑 𝑖+1 , . . . , 𝑑 𝑛 ) = π‘Œ 𝑖 (𝑑 𝑖 )

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Flashcard 7093119487244

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Question

The Fundamental Problem of Causal Inference:

It is impossible to observe all [...] for a given individual

Answer
potential outcomes

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The Fundamental Problem of Causal Inference: It is impossible to observe all potential outcomes for a given individual

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Flashcard 7093121060108

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Question
No interference means that my outcome is [...] by anyone else’s treatment.
Answer
unaffected

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No interference means that my outcome is unaffected by anyone else’s treatment.

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Flashcard 7093122895116

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Question
The [...] that you do not (and cannot) observe are known as counterfactuals because they are counter to fact (reality)
Answer
potential outcomes

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The potential outcomes that you do not (and cannot) observe are known as counterfactuals because they are counter to fact (reality)

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Flashcard 7093127875852

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Question
The idea is that although the treatment and potential outcomes may be [...] associated (due to confounding), within levels of 𝑋 , they are not associated
Answer
unconditionally

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The idea is that although the treatment and potential outcomes may be unconditionally associated (due to confounding), within levels of 𝑋 , they are not associated

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Flashcard 7093130759436

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Question
[...] (noun) is an approximation of some estimand, which we get using data
Answer
An estimate

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An estimate (noun) is an approximation of some estimand, which we get using data

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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)
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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 u

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Flashcard 7093137313036

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Question
not only is association [...], but causation is a sub-category of association. That’s why association and causation both flow along directed paths.
Answer
not causation

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not only is association not causation, but causation is a sub-category of association. That’s why association and causation both flow along directed paths.

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Flashcard 7093139148044

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Question
[...] graphical models are statistical models while causal graphical models are causal models.
Answer
Probabilistic

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Probabilistic graphical models are statistical models while causal graphical models are causal models.

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Flashcard 7093140983052

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Question
A graph is a collection of nodes (also called β€œ[...]”) and edges that connect the nodes.
Answer
vertices

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A graph is a collection of nodes (also called β€œvertices”) and edges that connect the nodes.

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Flashcard 7093145177356

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[unknown IMAGE 7093154876684] #DAG #causal #edx #has-images
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[unknown IMAGE 7093189741836] #DAG #causal #edx #has-images
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[unknown IMAGE 7093205732620] #DAG #causal #edx #has-images
For example, suppose that the causal DAG includes an unmeasured common cause of A and Y, U and also a measured variable L that is an effect of U. In those cases, it is generally better to adjust for L, because even though adjusting for L will not eliminate all confounding by U, it will typically eliminate some of the confounding by U. In those cases we say that L is a surrogate confounder or a proxy confounder. In summary, in any given causal DAG confounding is an absolute concept, but confounder is a relative concept
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Flashcard 7093214121228

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Question
Unfortunately, even if we only condition on pretreatment co- variates, we can still induce [...]. 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
collider bias

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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-causal association can flow

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Flashcard 7093215956236

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Question

The Positivity-Unconfoundedness Tradeoff

This is related to the curse of dimensionality. As each subgroup gets smaller, there is a higher and higher chance that either the whole subgroup will have treatment or the whole subgroup will have control. For example, once the size of any subgroup has decreased to [...], positivity is guaranteed to not hold.



Answer
one

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ller, there is a higher and higher chance that either the whole subgroup will have treatment or the whole subgroup will have control. For example, once the size of any subgroup has decreased to <span>one, positivity is guaranteed to not hold. <span>

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We present new research that brings clarity and a fact base to the shortcomings of survey-based measurement systems. We then examine how a few leaders have implemented data-driven CX systems and in turn reduced churn, boosted revenue, and lowered cost to serve.
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In this article, we explore how data and analytics are beginning to transform the art and science of customer experience. We present new research that brings clarity and a fact base to the shortcomings of survey-based measurement systems. We then examine how a few leaders have implemented data-driven CX systems and in turn reduced churn, boosted revenue, and lowered cost to serve. We end with insight on how to get started, including four key steps for CX leaders as they transition toward data-driven insight and action.

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Disadvantags of survey-based CX measurement

2. Reactive: Surveys are a backward-looking tool in a world where customers expect their concerns to be resolved increasingly quickly.

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2. Reactive: Surveys are a backward-looking tool in a world where customers expect their concerns to be resolved increasingly quickly. Nearly two-thirds of respondents ranked the ability to act on CX issues in near real time as among their top three priorities, but only 13 percent of leaders expressed certainty that th

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Disadvantags of survey-based CX measurement

1. Limited: The typical CX survey samples only 7 percent of a company’s customers

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Disadvantags of survey-based CX measurement 1. Limited: The typical CX survey samples only 7 percent of a company’s customers, providing an extremely limited view of what customers experience and value. In fact, only 13 percent of the CX leaders we surveyed expressed full confidence that their CX measurement s

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Flashcard 7093228539148

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Question

Rather, my outcome is only a function of my own treatment. We’ve been using this assumption implicitly throughout this chapter. We’ll now formalize it.

Assumption 2.4 ([...]) π‘Œ 𝑖 (𝑑 1 , . . . , 𝑑 π‘–βˆ’1 , 𝑑 𝑖 , 𝑑 𝑖+1 , . . . , 𝑑 𝑛 ) = π‘Œ 𝑖 (𝑑 𝑖 )

Answer
No Interference

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Rather, my outcome is only a function of my own treatment. We’ve been using this assumption implicitly throughout this chapter. We’ll now formalize it. Assumption 2.4 (No Interference) π‘Œ 𝑖 (𝑑 1 , . . . , 𝑑 π‘–βˆ’1 , 𝑑 𝑖 , 𝑑 𝑖+1 , . . . , 𝑑 𝑛 ) = π‘Œ 𝑖 (𝑑 𝑖 )

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Flashcard 7093279132940

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[unknown IMAGE 7093283327244] #DAG #causal #edx #has-images
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Flashcard 7093288045836

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Question

Two methods of controlling confounding:

Metod one: measure enough variables to block all backdoor paths

What is one of the names of these technics?

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Question
Controlling for L?
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#DAG #causal #edx
Inverse probability matching is in fact just one of the group of so called G-methods
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#DAG #causal #edx
So all these methods for confounding adjustment -- stratification, matching, inverse probability weighting, G-formula, G-estimation -- have two things in common. First, they require data on the confounders that block the backdoor path. If those data are available, then the choice of one of these methods over the others is often a matter of personal taste. Unless the treatment is time-varying -- then we have to go to G-methods
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[unknown IMAGE 7093318454540] #DAG #causal #edx #has-images
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#DAG #causal #edx
the most important take-home message: we need expert knowledge to determine if we should adjust for a variable. The statistical criteria are insufficient to characterize confounding and confounders. Of course, in many cases we don't have enough expert knowledge to draw the true causal DAG that represents a causal structure of treatment A, outcome Y, and potential confounder L. In those cases we may propose several possible causal DAGs without being able choose a particular one. And that's fine, because those causal DAGs that we propose allow us to identify inconsistencies between our beliefs and our actions. For example, suppose L is fetal death. We don't know the true causal DAG, we propose seven causal DAGs. Suppose that L does not help block a backdoor path in any of the seven DAGs, then we will not adjust for L, even if L were strongly associated with A and Y.
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#DAG #causal #edx
we have not been able to come up with a story in which L helps reduce confounding. In other words, we will not adjust for a variable unless we believe it may be a confounder. Our beliefs and our actions will be internally consistent when we use causal graph
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[unknown IMAGE 7093324483852] #DAG #causal #edx #has-images #inference
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