Edited, memorised or added to reading list

on 04-Jul-2022 (Mon)

Do you want BuboFlash to help you learning these things? Click here to log in or create user.

#DAG #causal #inference
This is not a philosophy book. We remain agnostic about metaphysical concepts like causality and cause. Instead, we focus on the identification and estimation of causal effects in populations, that is, numerical quantities that measure changes in the distribution of an outcome under different interventions.
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

pdf

cannot see any pdfs




#ML_in_Action #learning #machine #software-engineering
Part 3 (chapters 14–16) focuses on “the after”: specifically, considerations related to streamlining production release, retraining, monitoring, and attribution for a project. With examples focused on A/B testing, feature stores, and a passive retraining system, you’ll be shown how to implement systems and architectures that can ensure that you’re building the minimally complex solution to solve a business problem with ML
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

pdf

cannot see any pdfs




Flashcard 7101938535692

Tags
#causality #has-images #statistics


Question
Consider the following general kind of path, where → · · · → denotes a directed path: 𝑇 → · · · → 𝑍 ← · · · ← 𝑌 . Conditioning on 𝑍 , or any descendant of 𝑍 in a path like this, will induce [...] bias
Answer
collider

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
an> Consider the following general kind of path, where → · · · → denotes a directed path: 𝑇 → · · · → 𝑍 ← · · · ← 𝑌 . Conditioning on 𝑍 , or any descendant of 𝑍 in a path like this, will induce <span>collider bias <span>

Original toplevel document (pdf)

cannot see any pdfs







Flashcard 7101940370700

Question
Why use a [...] to ask customers about their experiences when data about customer interactions can be used to predict satisfaction?
Answer
survey

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Why use a survey to ask customers about their experiences when data about customer interactions can be used to predict satisfaction?

Original toplevel document (pdf)

cannot see any pdfs







Disadvantages of survey-based CX systems

4. Unfocused: As one executive at a large financial-services company put it, “The association between survey-based scores and business outcomes is not well understood, and, as a result, many parts of the organization simply claim a business impact from their CX initiatives with no real evidence.”

statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
4. Unfocused: As one executive at a large financial-services company put it, “The association between survey-based scores and business outcomes is not well understood, and, as a result, many parts of the organization simply claim a business impact from their CX initiatives with no real evidence.” Several companies have recently come under fire for basing investment decisions on a survey-based score alone. Remarkably, of the CX leaders we surveyed, only 4 percent said that their

Original toplevel document (pdf)

cannot see any pdfs




Flashcard 7101945613580

Tags
#causality #statistics
Question

Assumptions of causal inference:

1. Unconfoundedness (Assumption 2.2)

2. Positivity (Assumption 2.3)

3. No interference (Assumption 2.4)

4. [...] (Assumption 2.5)

Answer
Consistency

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
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)

Original toplevel document (pdf)

cannot see any pdfs







#DAG #causal #inference
we focus on the identification and estimation of causal effects in populations, that is, numerical quantities that measure changes in the distribution of an outcome under different interventions.
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on


Parent (intermediate) annotation

Open it
This is not a philosophy book. We remain agnostic about metaphysical concepts like causality and cause. Instead, we focus on the identification and estimation of causal effects in populations, that is, numerical quantities that measure changes in the distribution of an outcome under different interventions.

Original toplevel document (pdf)

cannot see any pdfs




#abm #agent-based #priority #rooftop-solar #simulation #synthetic-data
agent-based models are often not developed explicitly for prediction, and are generally not validated as such. We therefore present a novel data-driven agent-based modeling framework, in which individual behavior model is learned by machine learning techniques, deployed in multi-agent systems and validated using a holdout sequence of collective adoption decisions.
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

pdf

cannot see any pdfs




#abm #agent-based #priority #rooftop-solar #simulation #synthetic-data
the California Solar Initiative (CSI), a rooftop solar subsidy program initiated in 2007 with the goal of creating 1940 megawatts of solar capacity by 2016 [12]. The CSI program has been touted as a great success, and it certainly seems so: over 2000 megawatts have been installed to date. However, in a rigorous sense, success would have to be measured in comparison to some baseline; for ex- ample, in comparison to the same world, but without incentives. Of course, such an experiment is impossible in practice. However, in principle, insight can be drawn by sensitivity analysis based on hypothetical solar diffusion model. What is the most appropriate modeling methodology to build a highly robust solar diffusion mode
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

pdf

cannot see any pdfs




#abm #agent-based #priority #rooftop-solar #simulation #synthetic-data
Agent-based modeling (ABM) has long been a common framework of choice for studying aggregate, or emergent, properties of complex systems as they arise from microbehaviors of a multitude of agents in social and economic contexts [7,30,35]. ABM appears well-suited to policy experimentation of just the kind needed for the rooftop solar market. Indeed, there have been several attempts to develop agent-based models of solar adoption trends [14,32, 38]. Both traditional agent-based modeling, as well as the specific models developed for solar adoption, use data to calibrate aspects of the models (for example, features of the social network, such as density, are made to match real networks), but not the entire model. More importantly, validation is of- ten qualitative, or, if quantitative, using the same data as used for calibration. The weakness of validation makes those models less eligible as a reliable policy experiment tool.
statusnot read reprioritisations
last reprioritisation on suggested re-reading day
started reading on finished reading on

pdf

cannot see any pdfs