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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Do As They Did: Peer Effects Explain Adoption of Conservation Agriculture in Malawi
TL;DR: This paper conducted a study in the specific context of Malawi, using ethnographic interviewing to draw out possible decision criteria and machine learning to identify their explanatory power, and found that adoption by neighbors (i.e., peer effects) matters, with possible implications for the overall cost of encouraging CA adoption as it is taken up across a landscape.
Posted Content
Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform
TL;DR: This work finds that the introduction of a machine translation system has significantly increased international trade on this platform, increasing exports by 17.5% and providing causal evidence that language barriers significantly hinder trade.
Journal ArticleDOI
Reinforcement Learning in Economics and Finance
TL;DR: A state-of-the-art of reinforcement learning techniques are proposed, and applications in economics, game theory, operation research and finance are presented.
Journal ArticleDOI
Upward and downward bias when measuring inequality of opportunity
TL;DR: In this paper, the authors proposed a simple criterion to select the best econometric model which balances between the two sources of bias, a well-known downward bias, due to partial observability of cir- cumstances that affect individual outcome, and an upward bias, which is the consequence of sampling variance.
Journal ArticleDOI
Real estate price estimation in French cities using geocoding and machine learning
Dieudonné Tchuente,Serge Nyawa +1 more
TL;DR: The results reveal that neural networks and random forest techniques particularly outperform other methods when geocoding features are not accounted for, while random forest, adaboost and gradient boosting perform well when geocese features are considered.
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