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Found 5 papers, 0 papers with code
On Counterfactual Data Augmentation Under Confounding
Counterfactual data augmentation has recently emerged as a method to mitigate confounding biases in the training data.
Counterfactual Generation Under Confounding
Given a causal generative process, we formally characterize the adverse effects of confounding on any downstream tasks and show that the correlation between generative factors (attributes) can be used to quantitatively measure confounding between generative factors.
Divided We Rule: Influencer Polarization on Twitter During Political Crises in India
Influencers are key to the nature and networks of information propagation on social media.
Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals
We present a method for generating counterfactuals by incorporating a structural causal model (SCM) in an improved variant of Adversarially Learned Inference (ALI), that generates counterfactuals in accordance with the causal relationships between attributes of an image.
Synthetic Event Time Series Health Data Generation
Due to the complexity of the real data, in which each patient visit is an event, we transform the data by using summary statistics to characterize the events for a fixed set of time intervals, to facilitate analysis and interpretability.
