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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Focusing on Double Vision: Are Proxy Means Tests Effective to Identify Future School Dropouts and the Poor?
TL;DR: The paper shows that using the outputs of the predictive model in conjunction with the PMT increases targeting effectiveness by identifying more students who are either poor or future dropouts, and provides one of the first machine learning applications of school dropout in a developing country.
Proceedings ArticleDOI
Data Selection and Machine Learning Algorithm Application Under the Background of Big Data
TL;DR: In this paper, the authors proposed a data selection and the application of machine learning algorithms in the context of big data, based on the analysis of the machine learning implementation methods, the construction process of random forests, and random group sampling integration algorithms.
Dissertation
How Markets, Policies and Consumers Influence the Transition to Clean Energy
TL;DR: Apland et al. as discussed by the authors presented a Ph.D. dissertation on applied economics and applied it to the University of Minnesota's Applied Economics Program (AEP) with a focus on economics.
A Machine Learning Approach to Measuring Climate Adaptation
TL;DR: In this paper , a debiased machine learning approach was proposed to measure the elasticity of short-run and long-run changes in damaging weather in corn and soy production in the United States.
Journal ArticleDOI
Assessing Workload in Using Electromyography (EMG)-based Prostheses.
Junho Park,Joseph Berman,Albert Dodson,Yunmei Liu,He Huang,David B. Kaber,Jaime Ruiz,Maryam Zahabi +7 more
TL;DR: In this paper , the authors investigated classification models for assessing cognitive workload in electromyography-based prosthetic devices with various types of input features including eye-tracking measures, task performance, and cognitive performance model (CPM) outcomes.
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