Machine Learning
An Applied Econometric Approach
Bibliographic Data
| ID | 4029901 |
|---|---|
| Authors | Sendhil Mullainathan (0000-0001-8508-4052, Sendhil Mullainathan is the Robert C. Waggoner Professor of Economics, Harvard University, Cambridge, Massachusetts.), Jann Spie, Jann Spiess (0000-0002-4120-8241, Jann Spiess is a PhD candidate in Economics, Harvard University, Cambridge, Massachusetts.) |
| Year | 2017 |
| Volume | 31 |
| Issue | 2 |
| Pages | 87-106 |
| Publication date | 2017-05-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | The Journal of Economic Perspectives (JOURNAL) |
| Journal identifiers | ISSN: 0895-3309 • E-ISSN: 1944-7965 |
| Publisher | American Economic Association (PUBLISHER • US) |
| DOI | 10.1257/jep.31.2.87 |
| OpenAlex | W2610886376 |
| Language | EN |
| Citations received | 197 |
| References cited | 34 |
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 presence y of a face from pixels x. This similarity to econometrics raises questions: How do these new empirical tools fit with what we know? As empirical economists, how can we use them? We present a way of thinking about machine learning that gives it its own place in the econometric toolbox. Machine learning not only provides new tools, it solves a different problem. Specifically, machine learning revolves around the problem of prediction, while many economic applications revolve around parameter estimation. So applying machine learning to economics requires finding relevant tasks. Machine learning algorithms are now technically easy to use: you can download convenient packages in R or Python. This also raises the risk that the algorithms are applied naively or their output is misinterpreted. We hope 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-and thus where they can be most usefully applied
Empirical research · Machine learning · Toolbox · Computer Science · Energy, Environment, and Transportation Policies · Forecasting Techniques and Applications · Impact of Light on Environment and Health · Mathematics · Artificial Intelligence
How urban amenity factors shape the geography of innovative enterprises
Surveying the Economic Mind
How Well Can the Migration Component of Regional Population Change be Predicted? A Machine Learning Approach Applied to German Municipalities
Measuring and testing vulnerability to food insecurity for prediction and targeting
The View from Above
Black Boxes that Curtail Human Flourishing are no Longer Available for Use in Artificial Intelligence (AI) Design
Inflation Forecasting for Pakistan in a Data-rich Environment
Spending reflects not only who we are but also who we are around
The Roots of Inequality
Who voted for Brexit? Individual and regional data combined
Econometric Methods for Program Evaluation
Human Decisions and Machine Predictions
Toward understanding the impact of artificial intelligence on labor
Machine Learning Methods That Economists Should Know About
Integrating explanation and prediction in computational social science
Artificial Intelligence (AI)
Measuring the predictability of life outcomes with a scientific mass collaboration
Explanatory and Causal Methodology in Sociology
Who voted for Brexit? A comprehensive district-level analysis
Marginal College Wage Premiums Under Selection Into Employment
Folklore
A multi-scale story of the diffusion of a new technology
Machine learning to analyze the social-ecological impacts of natural resource policy
Human capital portability and international student migration
No perfect storm for crop yield failure in Germany
A Risk-Based Approach in Rehabilitation of Water Distribution Networks
Income, psychological security, and subjective well-being in urban China
Routes to the Top
The Wells-Du Bois Protocol for Machine Learning Bias
Does Culture Pay? Compensating Differentials, Job Satisfaction, and Organizational Practices
Urban economics in a historical perspective
Forecasting future adoption rates of agroecological innovations using machine learning
Machine learning in public administration research
How to promote relative and absolute educational intergenerational mobility? A global study based on interpretable machine learning
Life expectancy and its determinants
Planning for equitable public health
Citizen perceptions and support for urban sustainability investments in a Business Improvement District
Exploring the mechanism of path-creating strategy for latecomers
Integrating machine learning into business and management in the age of artificial intelligence
Environmental-Health Vulnerability and Respiratory Mortality in Europe
Determinants and prediction of Chlamydia trachomatis re-testing and re-infection within 1 year among heterosexuals with chlamydia attending a sexual health clinic
Machine learning and public health policy evaluation
Comparative Evaluation of Estimated Private Rates of Return to General and Vocational Upper Secondary Education in Greece
Identifying Courses for Targeted Review Using GAP Analysis and Machine Learning
Total Least Squares Estimation in Hedonic House Price Models
Factors influencing farmers’ adaptation willingness under landslide risks
A philosophy of health
Piercing a Methodological Bubble
Climate change vulnerability assessment of dryland farmers and factors Identification using machine learning techniques
How does the entrepreneurship ecosystem foster tourism competitiveness
Come and say G’day
The causal effect of agritourism on farm survival
Double Lasso
Addressing sample selection bias for machine learning methods
Prediction of problematic social media use (PSU) using machine learning approaches
Early warning systems for more effective student counselling in higher education
Analyzing factors associated with student achievement in large-scale educational assessments
Machine Labor
Predicting household resilience with machine learning
Machine learning approaches to testing institutional hypotheses
Uncovering heterogeneous regional impacts of Chinese monetary policy
Big data forecasting of South African inflation
Public subsidies and innovation
Economic determinants of regional trade agreements revisited using machine learning
Forecasting oil prices with random forests
The determinants of health expenditure
Identifying high-risk elderly for suicide using machine learning
Identifying Politically Connected Firms
On the (Mis)Use of Machine Learning With Panel Data
Using Machine Learning to Create an Early Warning System for Welfare Recipients
What is at stake without high-stakes exams? Students’ evaluation and admission to college at the time of Covid-19
Does a district mandate matter for the behavior of politicians? An analysis of roll-call votes and parliamentary speeches
Revisiting forced migration
Territorial differences in access to prenatal care and health at birth
Local inequalities of the Covid-19 crisis
Micro-geographic property price and rent indices
Prediction from early childhood vocabulary to academic achievement at the end of compulsory schooling in Denmark
The relationship between internet use and depressive symptoms among elderly in China
Artificial intelligence in the field of economics
Gotham city. Predicting ‘corrupted’ municipalities with machine learning
Machine learning and the optimization of prediction-based policies
Forecasting credit ratings of decarbonized firms
Harnessing the power of machine learning analytics to understand food systems dynamics across development projects
Enhancing economic cycle forecasting based on interpretable machine learning and news narrative sentiment
Role of green finance instruments in shaping economic cycles
Tension in big data using machine learning
Predicting local taxation decision-making
Machine learning, artificial neural networks and social research
Embracing complexity in social science research
Towards the collaborative development of machine learning techniques in planning support systems – a Sydney example
Anticipatory macroeconomic governance
Housing, imputed rent, and household welfare
Estimating intergenerational income mobility on sub-optimal data
Computational Methods in Legal Analysis
On Concepts, Analytics, and Statistics in Comparative Policy Studies
An exploratory study of populism
Gender Equality, Governance and National Innovation Capability for Sustainable Development
Citizen Participation and Political Trust in Latin America and the Caribbean
Integrating Computer Prediction Methods in Social Science
Integrating Street Views, Satellite Imageries and Remote Sensing Data Into Economics and the Social Sciences
Problems with Instrumental Variables Estimation When the Correlation Between the Instruments and the Endogeneous Explanatory Variable is Weak
Recursive partitioning for heterogeneous causal effects
Prediction Policy Problems
Inference on Treatment Effects after Selection among High-Dimensional Controls
Clinical Versus Actuarial Judgment
Measuring Economic Growth from Outer Space
Improving propensity score weighting using machine learning
Combining satellite imagery and machine learning to predict poverty
Predicting poverty and wealth from mobile phone metadata
Instrumental Variables Regression with Weak Instruments
Split-Sample Instrumental Variables Estimates of the Return to Schooling
Text-Based Network Industries and Endogenous Product Differentiation
Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-Out-the-Vote Campaign
Big Data
The View from Above
| Unique citing works | 197 |
|---|---|
| Citations per year | 19,7 |
| Citation span | 2016 - 2026 (11) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 189 |