Machine Learning: An Applied Econometric Approach
Sendhil Mullainathan,Jann Spiess +1 more
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TLDR
This work presents a way of thinking about machine learning that gives it its own place in the econometric toolbox, and aims to make them conceptually easier to use by providing a crisper understanding of how these algorithms work, where they excel, and where they can stumble.Abstract:
Machines are increasingly doing “intelligent” things. Face recognition algorithms use a large dataset of photos labeled as having a face or not to estimate a function that predicts the pre...read more
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Machine learning for ecosystem services
Simon Willcock,Simon Willcock,Javier Martínez-López,Danny A. P. Hooftman,Kenneth J. Bagstad,Stefano Balbi,Alessia Marzo,Carlo Prato,Saverio Sciandrello,Giovanni Signorello,Brian Voigt,Ferdinando Villa,James M. Bullock,Ioannis N. Athanasiadis +13 more
TL;DR: It is concluded that DDM has a clear role to play when modelling ecosystem services, helping produce interdisciplinary models and holistic solutions to complex socio-ecological issues.
Journal ArticleDOI
What Is Different About Digital Strategy? From Quantitative to Qualitative Change
TL;DR: The recent attention paid to the challenge of digital transformation signals an inflection point in the impact of digital technology on the competitive landscape, and it is suggested that this transition to a digital-only competitive landscape is imminent.
Journal ArticleDOI
A practical guide to selecting models for exploration, inference, and prediction in ecology.
TL;DR: In this article, the authors argue that much confusion surrounding statistical model selection results from failing to first clearly specify the purpose of the analysis is due to the fact that many possible approaches and techniques for model selection, and the conflicting recommendations for their use, can be confusing.
Proceedings ArticleDOI
Measurement and Fairness
Abigail Z. Jacobs,Hanna Wallach +1 more
TL;DR: In this article, measurement modeling from the quantitative social sciences is proposed as a framework for understanding fairness in computational systems, and the authors argue that many of the harms discussed in the literature on fairness in computing systems are direct results of such mismatches.
Posted Content
Deep Learning for Financial Applications : A Survey
TL;DR: In this article, the authors provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today, and identify possible future implementations and highlighted the pathway for the ongoing research within the field.
References
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Journal ArticleDOI
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: The Elements of Statistical Learning: Data Mining, Inference, and Prediction as discussed by the authors is a popular book for data mining and machine learning, focusing on data mining, inference, and prediction.
Journal ArticleDOI
Problems with Instrumental Variables Estimation when the Correlation between the Instruments and the Endogenous Explanatory Variable is Weak
TL;DR: In this article, the use of instruments that explain little of the variation in the endogenous explanatory variables can lead to large inconsistencies in the IV estimates even if only a weak relationship exists between the instruments and the error in the structural equation.
Journal Article
On Model Selection Consistency of Lasso
Peng Zhao,Bin Yu +1 more
TL;DR: It is proved that a single condition, which is called the Irrepresentable Condition, is almost necessary and sufficient for Lasso to select the true model both in the classical fixed p setting and in the large p setting as the sample size n gets large.
Journal ArticleDOI
Clinical versus actuarial judgment
TL;DR: Research comparing these two approaches to decision-making shows the actuarial method to be superior, factors underlying the greater accuracy of actuarial methods, sources of resistance to the scientific findings, and the benefits of increased reliance on actuarial approaches are discussed.
Book
A Distribution-Free Theory of Nonparametric Regression
TL;DR: How to Construct Nonparametric Regression Estimates * Lower Bounds * Partitioning Estimates * Kernel Estimates * k-NN Estimates * Splitting the Sample * Cross Validation * Uniform Laws of Large Numbers