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Found 5 papers, 1 papers with code
Causal Analysis of the TOPCAT Trial: Spironolactone for Preserved Cardiac Function Heart Failure
We describe the results of applying causal discovery methods on the data from a multi-site clinical trial, on the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT).
Equality of Effort via Algorithmic Recourse
This paper proposes a method for measuring fairness through equality of effort by applying algorithmic recourse through minimal interventions.
Domain Knowledge in A*-Based Causal Discovery
Causal discovery has become a vital tool for scientists and practitioners wanting to discover causal relationships from observational data.
Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data
We propose a flexible and simple to use framework for generating time series data, which is aimed at developing, evaluating, and benchmarking time series causal discovery methods.
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures
We present a non-parametric Bayesian latent variable model capable of learning dependency structures across dimensions in a multivariate setting.
