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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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.
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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.
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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.
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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).
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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
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Understanding the micro-determinants of defensive behaviors against pollution
TL;DR: In this article, the authors used survey data to assess which types of individuals are most likely to engage in defensive behaviors and how this response varies over multiple types of environmental risk, and assess the economic determinants of these defensive decisions.
Posted Content
Multiway Cluster Robust Double/Debiased Machine Learning
TL;DR: In this article, a multiway cross-fitting algorithm was proposed for double/debiased machine learning under multiway clustered sampling environments, and a multway DML estimator based on this algorithm was developed.
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Augmenting Pre-Analysis Plans with Machine Learning
TL;DR: A framework for PAPs that capitalize on the availability of causal machine-learning techniques, in which researchers combine specific aspects of the analysis with ML for the flexible estimation of unspecific remainders is suggested.
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A re-evaluation of the term spread as a leading indicator
TL;DR: This paper decomposes the term spread into an expectation and a term premium component and evaluates the informational content of each component in forecasting the GDP growth rate and inflation in various forecasting horizons and introduces the Support Vector Regression (SVR) methodology from the field of Machine Learning as a novel forecasting method in the field.
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Large-Scale Estimates of LGBQ-Heterosexual Disparities in the Presence of Potentially Mischievous Responders: A Preregistered Replication and Comparison of Methods:
TL;DR: Although numerous survey-based studies have found that students who identify as lesbian, gay, bisexual, or questioning (LGBQ) have elevated risk for many negative academic, disciplinary, psychologi...