Semiparametric doubly robust targeted double machine learning: a review
EH Kennedy - Handbook of Statistical Methods for Precision …, 2024 - taylorfrancis.com
In this review, we cover the basics of efficient nonparametric parameter estimation (also
called functional estimation), with a focus on parameters that arise in causal inference …
called functional estimation), with a focus on parameters that arise in causal inference …
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 …
Adapting neural networks for the estimation of treatment effects
This paper addresses the use of neural networks for the estimation of treatment effects from
observational data. Generally, estimation proceeds in two stages. First, we fit models for the …
observational data. Generally, estimation proceeds in two stages. First, we fit models for the …
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 …
Causal fairness analysis: a causal toolkit for fair machine learning
D Plečko, E Bareinboim - Foundations and Trends® in …, 2024 - nowpublishers.com
Decision-making systems based on AI and machine learning have been used throughout a
wide range of real-world scenarios, including healthcare, law enforcement, education, and …
wide range of real-world scenarios, including healthcare, law enforcement, education, and …
Causal fairness analysis
D Plecko, E Bareinboim - arXiv preprint arXiv:2207.11385, 2022 - arxiv.org
Decision-making systems based on AI and machine learning have been used throughout a
wide range of real-world scenarios, including healthcare, law enforcement, education, and …
wide range of real-world scenarios, including healthcare, law enforcement, education, and …
Counterfactual risk assessments, evaluation, and fairness
Algorithmic risk assessments are increasingly used to help humans make decisions in high-
stakes settings, such as medicine, criminal justice and education. In each of these cases, the …
stakes settings, such as medicine, criminal justice and education. In each of these cases, the …
Adapting text embeddings for causal inference
Does adding a theorem to a paper affect its chance of acceptance? Does labeling a post
with the author's gender affect the post popularity? This paper develops a method to …
with the author's gender affect the post popularity? This paper develops a method to …
Fairness in risk assessment instruments: Post-processing to achieve counterfactual equalized odds
In domains such as criminal justice, medicine, and social welfare, decision makers
increasingly have access to algorithmic Risk Assessment Instruments (RAIs). RAIs estimate …
increasingly have access to algorithmic Risk Assessment Instruments (RAIs). RAIs estimate …
Causal inference for social network data
We describe semiparametric estimation and inference for causal effects using observational
data from a single social network. Our asymptotic results are the first to allow for …
data from a single social network. Our asymptotic results are the first to allow for …