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stat.ML2024★ 1 cited
Continuous Bayesian Model Selection for Multivariate Causal Discovery
Anish Dhir, Ruby Sedgwick, Avinash Kori +2
Current causal discovery approaches require restrictive model assumptions in the absence of interventional data to ensure structure identifiability. These assumptions often do not…
stat.ML2023
Bivariate Causal Discovery using Bayesian Model Selection
Anish Dhir, Samuel Power, Mark van der Wilk
Much of the causal discovery literature prioritises guaranteeing the identifiability of causal direction in statistical models. For structures within a Markov equivalence class, th…
stat.ML2019
Integrating overlapping datasets using bivariate causal discovery
Anish Dhir, Ciarán M. Lee
Causal knowledge is vital for effective reasoning in science, as causal relations, unlike correlations, allow one to reason about the outcomes of interventions. Algorithms that can…