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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Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning
Linden McBride,Austin Nichols +1 more
TL;DR: Evidence is presented that prioritizing minimal out- of-sample error, identified through cross-validation and stochastic ensemble methods, in PMT tool development can substantially improve the out-of-sample performance of these targeting tools.
Journal ArticleDOI
Targeting Policy-Compliers with Machine Learning: An Application to a Tax Rebate Programme in Italy
TL;DR: This paper proposes an application of ML targeting that uses the massive tax rebate scheme introduced in Italy in 2014 to target the policy-compliers.
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How Do Individuals Repay Their Debt? The Balance-Matching Heuristic
TL;DR: In this paper, the authors study how individuals repay their debt using linked data on multiple credit cards, and they show that repayments are consistent with a balance-matching heuristic under which the share of repayments on each card is matched to the share in each card.
ReportDOI
Targeting with In-Kind Transfers: Evidence from Medicaid Home Care.
TL;DR: It is found that in-kind provision significantly reduces the value of the transfer to recipients while targeting a small fraction of the eligible population that is sicker and has fewer informal caregivers than the average eligible.
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Shapley regressions: A framework for statistical inference on machine learning models.
TL;DR: It is shown that universal approximators from machine learning are estimation consistent and introduced hypothesis tests for individual variable contributions, model bias and parametric functional forms, which strengthens the case for the use of machine learning to inform decisions.