Machine learning methods that economists should know about
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 …
econometrics. First we discuss the differences in goals, methods, and settings between the …
A brief review of random forests for water scientists and practitioners and their recent history in water resources
Random forests (RF) is a supervised machine learning algorithm, which has recently started
to gain prominence in water resources applications. However, existing applications are …
to gain prominence in water resources applications. However, existing applications are …
Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects (with discussion)
This paper presents a novel nonlinear regression model for estimating heterogeneous
treatment effects, geared specifically towards situations with small effect sizes …
treatment effects, geared specifically towards situations with small effect sizes …
Orthogonal statistical learning
DJ Foster, V Syrgkanis - The Annals of Statistics, 2023 - projecteuclid.org
Orthogonal statistical learning Page 1 The Annals of Statistics 2023, Vol. 51, No. 3, 879–908
https://doi.org/10.1214/23-AOS2258 © Institute of Mathematical Statistics, 2023 ORTHOGONAL …
https://doi.org/10.1214/23-AOS2258 © Institute of Mathematical Statistics, 2023 ORTHOGONAL …
Invariance, causality and robustness
P Bühlmann - Statistical Science, 2020 - JSTOR
We discuss recent work for causal inference and predictive robustness in a unifying way.
The key idea relies on a notion of probabilistic invariance or stability: it opens up new …
The key idea relies on a notion of probabilistic invariance or stability: it opens up new …
Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence
We investigate the finite-sample performance of causal machine learning estimators for
heterogeneous causal effects at different aggregation levels. We employ an empirical Monte …
heterogeneous causal effects at different aggregation levels. We employ an empirical Monte …
Random forest prediction intervals
H Zhang, J Zimmerman, D Nettleton… - The American …, 2020 - Taylor & Francis
Random forests are among the most popular machine learning techniques for prediction
problems. When using random forests to predict a quantitative response, an important but …
problems. When using random forests to predict a quantitative response, an important but …
A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting
G Papacharalampous, H Tyralis - Frontiers in Water, 2022 - frontiersin.org
Probabilistic forecasting is receiving growing attention nowadays in a variety of applied
fields, including hydrology. Several machine learning concepts and methods are notably …
fields, including hydrology. Several machine learning concepts and methods are notably …
A review of predictive uncertainty estimation with machine learning
H Tyralis, G Papacharalampous - Artificial Intelligence Review, 2024 - Springer
Predictions and forecasts of machine learning models should take the form of probability
distributions, aiming to increase the quantity of information communicated to end users …
distributions, aiming to increase the quantity of information communicated to end users …
Cate meets ml: Conditional average treatment effect and machine learning
D Jacob - Digital Finance, 2021 - Springer
For treatment effects—one of the core issues in modern econometric analysis—prediction
and estimation are two sides of the same coin. As it turns out, machine learning methods are …
and estimation are two sides of the same coin. As it turns out, machine learning methods are …