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Found 9 papers, 1 papers with code
Debiasing Machine Unlearning with Counterfactual Examples
This bias emerges from two main sources: (1) data-level bias, characterized by uneven data removal, and (2) algorithm-level bias, which leads to the contamination of the remaining dataset, thereby degrading model accuracy.
Causal Inference Using LLM-Guided Discovery
At the core of causal inference lies the challenge of determining reliable causal graphs solely based on observational data.
On Counterfactual Data Augmentation Under Confounding
Counterfactual data augmentation has recently emerged as a method to mitigate confounding biases in the training data.
Towards Learning and Explaining Indirect Causal Effects in Neural Networks
Recently, there has been a growing interest in learning and explaining causal effects within Neural Network (NN) models.
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation
Methods based on potential outcomes framework solve this problem by exploiting inductive biases and heuristics from causal inference.
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.
On Causally Disentangled Representations
Finally, we perform an empirical study on state of the art disentangled representation learners using our metrics and dataset to evaluate them from causal perspective.
Matching Learned Causal Effects of Neural Networks with Domain Priors
A trained neural network can be interpreted as a structural causal model (SCM) that provides the effect of changing input variables on the model's output.
Causality in Neural Networks -- An Extended Abstract
Causal reasoning is the main learning and explanation tool used by humans.
