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Found 4 papers, 2 papers with code
Results on Counterfactual Invariance
In this paper we provide a theoretical analysis of counterfactual invariance.
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
A directed acyclic graph (DAG) provides valuable prior knowledge that is often discarded in regression tasks in machine learning.
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects
These improved estimators are inspired by doubly robust estimators of the causal mean, using a similar form within the kernel space.
Selection, Ignorability and Challenges With Causal Fairness
In order to achieve this, we must have causal models that are able to capture what someone would be like if we were to counterfactually change these traits.
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