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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Asymmetric or Incomplete Information about Asset Values
TL;DR: The results suggest the previously reported agent-owned premiums suffer from an omitted variable bias, which prior studies incorrectly ascribe to market distortions associated with asymmetric information.
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Microstructure in the Machine Age
TL;DR: In this article, a machine learning algorithm is applied to predict and explain modern market microstructure phenomena, and the results are derived using 87 of the most liquid futures contracts across all asset classes.
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How Do Individuals Repay Their Debt? The Balance-Matching Heuristic
TL;DR: In this article, the authors study how individuals repay their debt using linked data on multiple credit cards, and they show that repayments are consistent with a balance-matching heuristic under which the share of repayments on each card is matched to the share in each card.
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Quantifying causality in data science with quasi-experiments
TL;DR: Approaches to causality that are popular in econometrics and that exploit (quasi) random variation in existing data are reviewed and how they can be combined with machine learning to answer causal questions within typical data science settings are shown.
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Introduction to the Special Collection on the Fragile Families Challenge
TL;DR: The Fragile Families Challenge as mentioned in this paper is a scientific mass collaboration designed to measure and understand the predictability of life trajectories, and participants in the challenge created predictive models to predict future trajectories.
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