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
Citations
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Higher education systems and regional economic development in Europe: A combined approach using econometric and machine learning methods
TL;DR: In this article , the authors examined the extent to which the performance and characteristics of higher education systems (HESs) influence regional economic development in 29 European countries from 2014 to 2016.
Journal ArticleDOI
A philosophy of health: life as reality, health as a universal value.
TL;DR: A Philosophy of Health is introduced by proposing that two general functions— precision and variation —impact population health at biological, behavioral, and social levels, and concludes that integrating functions, rather than separated structures drive healthy publics.
Journal ArticleDOI
Researcher reasoning meets computational capacity: Machine learning for social science.
TL;DR: In this article , the authors present a few guiding principles and promising approaches where they see particular potential for machine learning to transform social science inquiry and conclude that machine learning tools are increasingly accessible, worthy of attention, and ready to yield new discoveries for social research.
Dissertation
Essays on conditional cash transfers, targeting and educational outcomes: evidence from Chile
TL;DR: In this article, the authors investigated the impact of conditional cash transfers on the performance of primary and secondary education in Chile. But their focus was on the negative impact of the conditional cash transfer on future attendance and average grade.
Proceedings ArticleDOI
The Congressional Classification Challenge: Domain Specificity and Partisan Intensity
TL;DR: Surprisingly, it is found that the cross-domain learning performance, benchmarking the ability to generalize from one of these datasets to another, is in general poor, even though the algorithms perform very well in within-dataset cross-validation tests.
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