Causal machine learning for predicting treatment outcomes
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment
outcomes including efficacy and toxicity, thereby supporting the assessment and safety of …
outcomes including efficacy and toxicity, thereby supporting the assessment and safety of …
Causal inference methods for combining randomized trials and observational studies: a review
The supplementary material contains details on treatment effect estimation performed
separately on RCT data (Section A) and on observational data (Section B), derivations of the …
separately on RCT data (Section A) and on observational data (Section B), derivations of the …
Demystifying statistical learning based on efficient influence functions
Abstract Evaluation of treatment effects and more general estimands is typically achieved via
parametric modeling, which is unsatisfactory since model misspecification is likely. Data …
parametric modeling, which is unsatisfactory since model misspecification is likely. Data …
Causal Machine Learning and its use for public policy
M Lechner - Swiss Journal of Economics and Statistics, 2023 - Springer
In recent years, microeconometrics experienced the 'credibility revolution', culminating in the
2021 Nobel prices for David Card, Josh Angrist, and Guido Imbens. This 'revolution'in how to …
2021 Nobel prices for David Card, Josh Angrist, and Guido Imbens. This 'revolution'in how to …
[HTML][HTML] Investment in intangible assets and economic complexity
JM Uribe - Research Policy, 2025 - Elsevier
We study the nexus between a country's economic complexity and its investment level in
intangible assets. Our data spans 27 countries, all sector classifications and 8 intangible …
intangible assets. Our data spans 27 countries, all sector classifications and 8 intangible …
Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study
P Bach, O Schacht, V Chernozhukov… - Causal Learning …, 2024 - proceedings.mlr.press
Proper hyperparameter tuning is essential for achieving optimal performance of modern
machine learning (ML) methods in predictive tasks. While there is an extensive literature on …
machine learning (ML) methods in predictive tasks. While there is an extensive literature on …
Comprehensive Causal Machine Learning
M Lechner, J Mareckova - arXiv preprint arXiv:2405.10198, 2024 - arxiv.org
Uncovering causal effects at various levels of granularity provides substantial value to
decision makers. Comprehensive machine learning approaches to causal effect estimation …
decision makers. Comprehensive machine learning approaches to causal effect estimation …
Double machine learning and automated confounder selection: A cautionary tale
Double machine learning (DML) has become an increasingly popular tool for automated
variable selection in high-dimensional settings. Even though the ability to deal with a large …
variable selection in high-dimensional settings. Even though the ability to deal with a large …
Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables
J Runge - Advances in Neural Information Processing …, 2021 - proceedings.neurips.cc
The problem of selecting optimal backdoor adjustment sets to estimate causal effects in
graphical models with hidden and conditioned variables is addressed. Previous work has …
graphical models with hidden and conditioned variables is addressed. Previous work has …
Ananke: A python package for causal inference using graphical models
We implement Ananke: an object-oriented Python package for causal inference with
graphical models. At the top of our inheritance structure is an easily extensible Graph class …
graphical models. At the top of our inheritance structure is an easily extensible Graph class …