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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From Predictive Algorithms to Automatic Generation of Anomalies
TL;DR: Mullainathan et al. as mentioned in this paper developed an econometric framework for anomaly generation and two algorithmic procedures to generate anomalies (if they exist) when provided a formal theory and data that the theory seeks to explain.
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Predicting nutritional status for women of childbearing age from their economic, health, and demographic features: A supervised machine learning approach
TL;DR: In this article , the authors used a series of supervised machine learning (SML) techniques to predict the BMI and the risks of malnutrition outcomes for Bangladeshi women of childbearing age from their economic, health and demographic features.
Journal ArticleDOI
Changing Electricity Markets: Quantifying the Price Effects of Greening the Energy Matrix
Making the Most of Supervised Machine Learning in Strategy
Riitta Katila,Philipp Reineke +1 more
TL;DR: This paper spotlighted supervised machine learning and its application to many problems in strategy that inherently involve prediction.
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.
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.
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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