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Found 9 papers, 1 papers with code
Evaluating the Fairness of Discriminative Foundation Models in Computer Vision
We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP), that are used for labeling tasks.
Meaningful Causal Aggregation and Paradoxical Confounding
On the positive side, we show that cause-effect relations can be aggregated when the macro interventions are such that the distribution of micro states is the same as in the observational distribution; we term this natural macro interventions.
Explaining the root causes of unit-level changes
Existing methods of explainable AI and interpretable ML cannot explain change in the values of an output variable for a statistical unit in terms of the change in the input values and the change in the "mechanism" (the function transforming input to output).
DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
We introduce DoWhy-GCM, an extension of the DoWhy Python library, that leverages graphical causal models.
Why did the distribution change?
We describe a formal approach based on graphical causal models to identify the "root causes" of the change in the probability distribution of variables.
Discovering Reliable Causal Rules
Moreover, to discover reliable causal rules from a sample, we propose a conservative and consistent estimator of the causal effect, and derive an efficient and exact algorithm that maximises the estimator.
Quantifying intrinsic causal contributions via structure preserving interventions
We propose a notion of causal influence that describes the `intrinsic' part of the contribution of a node on a target node in a DAG.
Causal structure based root cause analysis of outliers
We describe a formal approach to identify 'root causes' of outliers observed in $n$ variables $X_1,\dots, X_n$ in a scenario where the causal relation between the variables is a known directed acyclic graph (DAG).
Causal Inference by Stochastic Complexity
The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov complexity.
