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Found 7 papers, 3 papers with code
The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
We adapt tools from causal sensitivity analysis to the FairML context, providing a general framework which (1) accommodates effectively any combination of fairness metric and bias that can be posed in the "oblivious setting"; (2) allows researchers to investigate combinations of biases, resulting in non-linear sensitivity; and (3) enables flexible encoding of domain-specific constraints and assumptions.
Is merging worth it? Securely evaluating the information gain for causal dataset acquisition
To the best of our knowledge, this work presents the first privacy-preserving method for dataset acquisition tailored to causal estimation.
The Role of Learning Algorithms in Collective Action
Collective action in machine learning is the study of the control that a coordinated group can have over machine learning algorithms.
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.
