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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Machine learning algorithm in a caloric view point of cosmology
TL;DR: The caloric VPG model (cVPG henceforth) is suitable for the statistical cosmology and can be used to make useful predictions for the future of the universe via the machine learning methods like the linear regression (LR Henceforth) algorithm.
Journal ArticleDOI
The Microstructure of Endogenous Liquidity Provision
TL;DR: In this paper, a nonlinear rational expectations equilibrium model with an ex post endogenous liquidity provision decision is proposed to explain a variety of financial market outcomes, including price impacts and the possibility of market breaks.
Posted ContentDOI
Machine learning methods to assess the effects of a non-lineardamage spectrum taking into account soil moisture on winter wheatyields in Germany
TL;DR: In this article, the authors used random forests to investigate such highly non-linear systems for predicting yield anomalies in winter wheat at district level in Germany, in order to take into account sub-seasonality, monthly features are used that explicitly take soil moisture into account in addition to extreme meteorological events.
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Causal Machine Learning and Business Decision Making
TL;DR: This study argues that causality is a critical boundary condition for the application of machine learning in a business analytical context and highlights the crucial role of theory in causal inference and offers a new perspective on human-machine interaction for data-augmented decision making.
Journal ArticleDOI
African Americans and COVID-19: Beliefs, behaviors and vulnerability to infection
TL;DR: How social, economic and physical conditions determine vulnerability to COVID-19 infection among African Americans is examined and implications for how healthcare organizations can address the needs of vulnerable populations during a health-related crisis are discussed.
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.
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