# Machine Learning Methods Economists Should Know About

arXiv: https://arxiv.org/abs/1903.10075 · HTML: https://mlsanity.com/paper/1903.10075/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2019.

## Built on by (most cited first, 30 of 64)

- [Leakage and the Reproducibility Crisis in ML-based Science](https://mlsanity.com/paper/2207.07048/) (2022)
- [Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects](https://mlsanity.com/paper/2111.07966/) (2021)
- [Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters](https://mlsanity.com/paper/2104.04103/) (2021)
- [Validating Causal Inference Methods](https://mlsanity.com/paper/2202.04208/) (2022)
- [The Relative Value of Prediction in Algorithmic Decision Making](https://mlsanity.com/paper/2312.08511/) (2023)
- [REFORMS: Reporting Standards for Machine Learning Based Science](https://mlsanity.com/paper/2308.07832/) (2023)
- [Generalization in Neural Networks: A Broad Survey](https://mlsanity.com/paper/2209.01610/) (2022)
- [A Survey of Deep Causal Models and Their Industrial Applications](https://mlsanity.com/paper/2209.08860/) (2022)
- [Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap](https://mlsanity.com/paper/2104.05762/) (2021)
- [Counterfactual Situation Testing: Uncovering Discrimination under Fairness given the Difference](https://mlsanity.com/paper/2302.11944/) (2023)
- [Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal](https://mlsanity.com/paper/2310.08672/) (2023)
- [Causal Inference on Outcomes Learned from Text](https://mlsanity.com/paper/2503.00725/) (2025)
- [Can LLMs Learn Macroeconomic Narratives from Social Media?](https://mlsanity.com/paper/2406.12109/) (2024)
- [Mining Causality: AI-Assisted Search for Instrumental Variables](https://mlsanity.com/paper/2409.14202/) (2024)
- [LABOR-LLM: Language-Based Occupational Representations with Large Language Models](https://mlsanity.com/paper/2406.17972/) (2024)
- [CausalNLP: A Practical Toolkit for Causal Inference with Text](https://mlsanity.com/paper/2106.08043/) (2021)
- [Disaggregated Interventions to Reduce Inequality](https://mlsanity.com/paper/2107.00593/) (2021)
- [On the Pointwise Behavior of Recursive Partitioning and Its Implications for Heterogeneous Causal Effect Estimation](https://mlsanity.com/paper/2211.10805/) (2022)
- [Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance](https://mlsanity.com/paper/2201.12692/) (2022)
- [Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance](https://mlsanity.com/paper/2301.07060/) (2023)
- [Deep Learning for Economists](https://mlsanity.com/paper/2407.15339/) (2024)
- [Distribution Shift in Airline Customer Behavior during COVID-19](https://mlsanity.com/paper/2111.14938/) (2021)
- [Macroeconomic Predictions using Payments Data and Machine Learning](https://mlsanity.com/paper/2209.00948/) (2022)
- [Adventures in Demand Analysis Using AI](https://mlsanity.com/paper/2501.00382/) (2024)
- [Moments of Causal Effects](https://mlsanity.com/paper/2505.04971/) (2025)
- [Calibrating doubly-robust estimators with unbalanced treatment assignment](https://mlsanity.com/paper/2403.01585/) (2024)
- [Counterfactual Situation Testing: From Single to Multidimensional Discrimination](https://mlsanity.com/paper/2502.01267/) (2025)
- [Integrating Unstructured Text into Causal Inference: Empirical Evidence from Real Data](https://mlsanity.com/paper/2602.14274/) (2026)
- [Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets](https://mlsanity.com/paper/2105.01426/) (2021)
- [Public Policymaking for International Agricultural Trade using Association Rules and Ensemble Machine Learning](https://mlsanity.com/paper/2111.07508/) (2021)

## Abstract (arXiv)

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.