Machine Learning: An Applied Econometric Approach
Sendhil Mullainathan,Jann Spiess +1 more
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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.
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The Theory is Predictive, but is it Complete? An Application to Human Perception of Randomness
TL;DR: In this article, the authors consider approaches motivated by machine learning algorithms as a means of constructing a benchmark for the best attainable level of prediction and illustrate their methods on the task of predicting human-generated random sequences.
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Dynamic Unstructured Bargaining with Private Information: Theory, Experiment, and Outcome Prediction via Machine Learning
TL;DR: It is confirmed that strike incidence is decreasing in the pie size, and two equilibria are derived that resolve the trade-off between equality and efficiency by favoring either equality or efficiency.
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Counterfactual Prediction with Deep Instrumental Variables Networks.
TL;DR: A recipe for combining ML algorithms to solve for causal effects in the presence of instrumental variables -- sources of treatment randomization that are conditionally independent from the response is provided.
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Machine Learning in Finance: A Topic Modeling Approach
TL;DR: This study provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena and showcases the benefits to finance researchers of the method of probabilistic modeling of topics for deep comprehension of a body of literature.
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Predicting Consumer Default: A Deep Learning Approach
TL;DR: A model to predict consumer default based on deep learning is developed and it is shown that the model consistently outperforms standard credit scoring models, even though it uses the same data.