F
Fredrik D. Johansson
Researcher at Chalmers University of Technology
Publications - 67
Citations - 2720
Fredrik D. Johansson is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Computer science & Causal inference. The author has an hindex of 20, co-authored 54 publications receiving 2023 citations. Previous affiliations of Fredrik D. Johansson include Technical University of Dortmund & Massachusetts Institute of Technology.
Papers
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Proceedings Article
Estimating individual treatment effect: generalization bounds and algorithms
TL;DR: In this paper, a family of algorithms for predicting individual treatment effect (ITE) from observational data, under the assumption known as strong ignorability, was proposed, where the algorithms learn a "balanced" representation such that the induced treated and control distributions look similar.
Posted Content
Learning Representations for Counterfactual Inference
TL;DR: A new algorithmic framework for counterfactual inference is proposed which brings together ideas from domain adaptation and representation learning and significantly outperforms the previous state-of-the-art approaches.
Journal ArticleDOI
Guidelines for reinforcement learning in healthcare
Omer Gottesman,Fredrik D. Johansson,Matthieu Komorowski,A. Aldo Faisal,David Sontag,Finale Doshi-Velez,Leo Anthony Celi,Leo Anthony Celi +7 more
TL;DR: New guidelines for reinforcement learning for decisions about patient treatment are provided that are hoped will accelerate the rate at which observational cohorts can inform healthcare practice in a safe, risk-conscious manner.
Posted Content
Estimating individual treatment effect: generalization bounds and algorithms
TL;DR: A novel, simple and intuitive generalization-error bound is given showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalized-error of that representation and the distance between the treated and control distributions induced by the representation.
Proceedings Article
Why Is My Classifier Discriminatory
TL;DR: In this article, the authors argue that the fairness of predictions should be evaluated in context of the data, and that unfairness induced by inadequate samples sizes or unmeasured predictive variables should be addressed through data collection, rather than by constraining the model.