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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Unveiling the Relationship between Economic Growth and Equality for Developing Countries
TL;DR: In this paper , the authors investigated the relationship between economic growth and inequality by employing the artificial neural network approach and revealed the underlying functional form of economic growth by using three-dimensional diagrams.
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Differentiable and Transportable Structure Learning
TL;DR: D-Struct is introduced which recovers transportability in the discovered structures through a novel architecture and loss function, while remaining completely differentiable.
Posted Content
Tax Morale and Perceived Intergenerational Mobility: a Machine Learning Predictive Approach
Rocco Caferra,Andrea Morone +1 more
TL;DR: In this article, the authors investigate the linkage between the perceived intergenerational mobility and the preferences for tax payment and find evidence of a strong negative relation between perceived mobility and tax cheating, suggesting that fairness in tax payment has also to be seen on the light of the perceived efficiency of the welfare state in providing more opportunities across generations.
Book
Leveraging Big Data Analytics to Improve Military Recruiting
TL;DR: In this paper, the authors identified current and desired data-enabled practices in military outreach and recruiting processes and identified ways that the U.S. Department of Defense and the services might be able to further deploy data-driven outreach and recruitment strategies and barriers to doing so that need to be addressed.
Posted Content
A longitudinal framework for predicting nonresponse in panel surveys
TL;DR: This study proposes a longitudinal framework for predicting nonresponse with machine learning and multiple panel waves and illustrates its application with data from a German probability-based mixed-mode panel, showing that aggregating information over multiplepanel waves can be used to build prediction models with competitive and robust performance over all test waves.
References
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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.
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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