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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The Economic Consequences of Algorithmic Discrimination: Theory and Empirical Evidence
TL;DR: The results emphasize that Artificial Intelligence systems' capabilities in overcoming information asymmetries and thereby enhancing welfare negatively depend on the degree of inherent algorithmic discrimination against specific groups in the population.
Posted Content
Proyección de la Inflación en Chile con Métodos de Machine Learning
TL;DR: In this article, the authors apply Machine Learning (ML) methods with big data to forecast the total and underlying CPI inflation in Chile and show that the ML methods do not gain in the inflation projection for the Chilean case in a consistent way on simple and univariate linear competitors such as the AR, the mean and the median of the past inflation, which have proven to be highly competitive.
Journal ArticleDOI
Learning When to Advise Human Decision Makers
Gali Noti,Yiling Chen +1 more
TL;DR: This work proposes the design of AI systems that interact with the human user in a two-sided manner and provide advice only when it is likely to be beneficial to the human in making their decision.
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Regression and Machine Learning Methods to Predict Discrete Outcomes in Accounting Research
Jake Krupa,Miguel Minutti-Meza +1 more
TL;DR: This work provides an overview of how to predict discrete outcomes with logistic regression and two machine learning models used in recent studies: support vector machines and gradient boosting and illustrates the elements of the prediction process.
Journal ArticleDOI
Estimating intergenerational income mobility on sub-optimal data: a machine learning approach
TL;DR: In this paper, a machine learning approach is proposed to improve the reliability and comparability of the estimates of intergenerational income mobility. But the approach is limited to the United States and South Africa.
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