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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Using Machine Learning to Target Treatment: The Case of Household Energy Use
TL;DR: In this article, causal forests are used to evaluate the heterogeneous treatment effects of repeated behavioral nudges towards household energy conservation, and the average response is a monthly electricity reduction of 9 kilowatt-hours (kWh), but the full distribution of responses ranges from -30 to + 10 kWh.
Journal ArticleDOI
Bias From Potentially Mischievous Responders on Large-Scale Estimates of Lesbian, Gay, Bisexual, or Questioning (LGBQ)-Heterosexual Youth Health Disparities.
Joseph R. Cimpian,Jennifer D. Timmer,Michelle Birkett,Rachel Marro,Blair Turner,Gregory Phillips +5 more
TL;DR: Using screener items in public health data sets and performing rigorous sensitivity analyses can support the validity of youth health estimates, particularly among boys, but bullying and suicidal ideation were unaffected.
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A comprehensive review on medical diagnosis using machine learning
TL;DR: A comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases to show the distribution of machine learning methods by medical disciplines.
Journal ArticleDOI
Who performs better? AVMs vs hedonic models
TL;DR: This is the first systematic review that collects all the articles produced on the subject done comparing the results obtained and concludes that machine learning models are more accurate than traditional regression analysis in their ability to predict value.
Journal ArticleDOI
In-Text Patent Citations: A User’s Guide
TL;DR: In this paper, the authors introduce, validate, and make publicly available a new data source for innovation research: scientific references in patent specifications, which are common and algorithmically extractable.
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