Machine Learning Methods Economists Should Know About

Abstract

We discuss the relevance of the recent Machine Learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods and settings between the ML literature and the traditional econometrics and statistics literatures. Then we discuss some specific methods from the machine learning literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and classification, unsupervised learning methods, as well as matrix completion methods. Finally, we highlight newly developed methods at the intersection of ML and econometrics, methods that typically perform better than either off-the-shelf ML or more traditional econometric methods when applied to particular classes of problems, problems that include causal inference for average treatment effects, optimal policy estimation, and estimation of the counterfactual effect of price changes in consumer choice models.

References in corpus (25)

• Efficient Estimation of Word Representations in Vector Space
• Distributed Representations of Words and Phrases and their Compositionality
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Rejoinder to "Least angle regression" by Efron et al
• Least Angle Regression
• Recursive Partitioning for Heterogeneous Causal Effects
• Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
• Matrix Completion Methods for Causal Panel Data Models
• Towards Principled Methods for Training Generative Adversarial Networks
• A Fast and Simple Algorithm for Training Neural Probabilistic Language Models
• Doubly Robust Policy Evaluation and Learning
• Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso
• Learning from Logged Implicit Exploration Data
• Counterfactual Prediction with Deep Instrumental Variables Networks
• Counterfactual Inference for Consumer Choice Across Many Product Categories
• Counterfactual Estimation and Optimization of Click Metrics for Search Engines
• Principal Components and Regularized Estimation of Factor Models
• Balanced Linear Contextual Bandits

Cited by in corpus (4)

• Supervised learning for the prediction of firm dynamics
• Analysing a built-in advantage in asymmetric darts contests using causal machine learning
• Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap
• Active labour market policies for the long-term unemployed: New evidence from causal machine learning