Machine Learning: An Applied Econometric Approach
Sendhil Mullainathan,Jann Spiess +1 more
Reads0
Chats0
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
More filters
Journal ArticleDOI
Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.
TL;DR: The algorithms of machine learning, which can sift through vast numbers of variables looking for combinations that reliably predict outcomes, will improve prognosis, displace much of the work of radiologists and anatomical pathologists, and improve diagnostic accuracy.
Journal ArticleDOI
Artificial Intelligence (AI) : Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy
Yogesh K. Dwivedi,Laurie Hughes,Elvira Ismagilova,Gert Aarts,Crispin Coombs,Tom Crick,Yanqing Duan,Rohita Dwivedi,John S. Edwards,Aled Eirug,Vassilis Galanos,P. Vigneswara Ilavarasan,Marijn Janssen,Paul Jones,Arpan Kumar Kar,Hatice Kizgin,Bianca Kronemann,Banita Lal,Biagio Lucini,Rony Medaglia,Kenneth Le Meunier-FitzHugh,Leslie Caroline Le Meunier-FitzHugh,Santosh K. Misra,Emmanuel Mogaji,Sujeet Kumar Sharma,Jang Bahadur Singh,Vishnupriya Raghavan,Ramakrishnan Raman,Nripendra P. Rana,Spyridon Samothrakis,Jak Spencer,Kuttimani Tamilmani,Annie Tubadji,Paul Walton,Michael D. Williams +34 more
TL;DR: This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.
Posted Content
The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning.
Sam Corbett-Davies,Sharad Goel +1 more
TL;DR: It is argued that it is often preferable to treat similarly risky people similarly, based on the most statistically accurate estimates of risk that one can produce, rather than requiring that algorithms satisfy popular mathematical formalizations of fairness.
Journal ArticleDOI
Financial time series forecasting with deep learning : A systematic literature review: 2005–2019
TL;DR: A comprehensive literature review on DL studies for financial time series forecasting implementations and grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM).
Journal ArticleDOI
Human Decisions and Machine Predictions
TL;DR: While machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.
References
More filters
Journal ArticleDOI
Text-Based Network Industries and Endogenous Product Differentiation
Gerard Hoberg,Gordon M. Phillips +1 more
TL;DR: The authors study how firms differ from their competitors using new time-varying measures of product similarity based on text-based analysis of firm 10-K product descriptions and find evidence that firm R&D and advertising are associated with subsequent differentiation from competitors.
Journal ArticleDOI
Improving propensity score weighting using machine learning.
TL;DR: The authors examine the performance of various CART‐based propensity score models using simulated data and suggest that ensemble methods, especially boosted CART, may be useful for propensity score weighting.
Journal ArticleDOI
Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err
TL;DR: It is shown that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster, and this phenomenon, which is called algorithm aversion, is costly, and it is important to understand its causes.
Journal ArticleDOI
Sparse models and methods for optimal instruments with an application to eminent domain
TL;DR: In this paper, preliminary results of this paper were presented at Chernozhukov's invited Cowles Foundation lecture at the Northern American meetings of the Econometric society in June of 2009.
Posted Content
Text-Based Network Industries and Endogenous Product Differentiation
Gerard Hoberg,Gordon M. Phillips +1 more
TL;DR: The authors study how firms differ from their competitors using new time-varying measures of product differentiation based on text-based analysis of product descriptions from 50,673 firm 10-K statements filed yearly with the Securities and Exchange Commission.