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Found 8 papers, 2 papers with code
Tightening Bounds on Probabilities of Causation By Merging Datasets
Existing work to further tighten these bounds by leveraging extra information either provides numerical bounds, symbolic bounds for fixed dimensionality, or requires access to multiple datasets that contain the same treatment and outcome variables.
Beyond Single-Feature Importance with ICECREAM
Which set of features was responsible for a certain output of a machine learning model?
Self-Compatibility: Evaluating Causal Discovery without Ground Truth
In this work, we propose a novel method for falsifying the output of a causal discovery algorithm in the absence of ground truth.
Toward Falsifying Causal Graphs Using a Permutation-Based Test
By comparing the number of inconsistencies with those on the surrogate baseline, we derive an interpretable metric that captures whether the DAG fits significantly better than random.
Bounding probabilities of causation through the causal marginal problem
In the absence of such assumptions, existing work requires multiple observations of datasets that contain the same treatment and outcome variables, in order to establish bounds on these probabilities.
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.
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.
Necessary and sufficient conditions for causal feature selection in time series with latent common causes
We study the identification of direct and indirect causes on time series and provide conditions in the presence of latent variables, which we prove to be necessary and sufficient under some graph constraints.
