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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Proceedings ArticleDOI
Towards a Just Theory of Measurement: A Principled Social Measurement Assurance Program for Machine Learning
TL;DR: A social measurement assurance program (sMAP) in which ML encourages expert deliberation on a given decision-making procedure by examining unanticipated or previously unexamined covariates, and applies Rawlsian principles of fairness to sMAP.
Journal ArticleDOI
The Hard Problem of Prediction for Conflict Prevention
TL;DR: In this paper , the authors proposed a framework to tackle conflict prevention, where topics are fed into a random forest to predict conflict risk, which is then integrated into a simple static framework in which a decision maker decides on the optimal number of interventions to minimize the total cost of conflict and intervention.
Journal ArticleDOI
Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment
Michael J. Weir,Thomas W. Sproul +1 more
TL;DR: In this paper, the authors conducted a choice experiment eliciting willingness-to-pay for salmon fillets with varying characteristics, including GM technology and GM feed, and developed a predictive model of consumer choice using LASSO (least absolute shrinkage and selection operator)-regularization applied to a mixed logit, incorporating risk perception, ambiguity preference, and other behavioral measures as potential predictors.
Journal ArticleDOI
Predictors of emergency department opioid administration and prescribing: A machine learning approach.
Molly McCann-Pineo,Molly McCann-Pineo,Julia Ruskin,Rehana Rasul,Rehana Rasul,Eugene Vortsman,Kristin Bevilacqua,Samantha S. Corley,Samantha S. Corley,Rebecca M. Schwartz +9 more
TL;DR: The utility of machine learning is demonstrated for understanding clinical predictors of opioid administration and prescribing in the ED, and its potential in informing standardized prescribing recommendations and guidelines is demonstrated.
Journal ArticleDOI
Estimating Parameters of Structural Models Using Neural Networks
Yanhao 'Max' Wei,Zhenling Jiang +1 more
TL;DR: This work shows this Neural Net Estimator (NNE) converges to Bayesian parameter posterior when the number of training datasets is sufficiently large, and examines the performance of NNE in two Monte Carlo studies.
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