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.
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.
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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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Oracle inequalities for multi-fold cross validation
TL;DR: The results are extended to penalized cross validation in order to control unbounded loss functions and applications include regression with squared and absolute deviation loss and classification under Tsybakov’s condition.
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Double machine learning for treatment and causal parameters
Victor Chernozhukov,Denis Chetverikov,Mert Demirer,Esther Duflo,Christian Hansen,Whitney K. Newey +5 more
TL;DR: The resulting method could be called a "double ML" method because it relies on estimating primary and auxiliary predictive models and achieves the fastest rates of convergence and exhibit robust good behavior with respect to a broader class of probability distributions than naive "single" ML estimators.
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Can one estimate the unconditional distribution of post-model-selection estimators?
Hannes Leeb,Benedikt M. Pötscher +1 more
TL;DR: In this article, the authors consider the problem of estimating the unconditional distribution of a post-model-selection estimator and show that no estimator for this distribution can be uniformly consistent (not even locally).
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Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine Learning Methods
TL;DR: It is shown that ML models with a large number of covariates are systematically more accurate than the benchmarks and the ML method that deserves more attention is the random forest model, which dominates all other models.
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A regularization approach to the many instruments problem
TL;DR: In this article, the authors proposed three modified instrumental variable estimators based on three different ways of inverting the covariance matrix of the instruments, which lead to a consistent nonparametric estimation of the optimal instrument.