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Found 4 papers, 3 papers with code
Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks
In this paper, we demonstrate that acceptance of explanations by the user is not a guarantee for a machine learning model to be robust against Clever Hans effects, which may remain undetected.
Human alignment of neural network representations
Linear transformations of neural network representations learned from behavioral responses from one dataset substantially improve alignment with human similarity judgments on the other two datasets.
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy.
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatments, or both.
