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Found 6 papers, 3 papers with code
Recursive Causal Discovery
Presence and identification of removable variables allow recursive approaches for causal discovery, a promising solution that helps to address the aforementioned challenges by reducing the problem size successively.
Learning Causal Graphs via Monotone Triangular Transport Maps
We study the problem of causal structure learning from data using optimal transport (OT).
Experimental Design for Causal Effect Identification
When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the system to learn the causal effect.
A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics
Unfortunately, they are unlikely to be sufficiently flexible to be able to adequately model real-world phenomena, and may yield biased estimates.
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias
The upper bound of our proposed approach and the lower bound at most differ by a factor equal to the number of variables in the worst case.
A Recursive Markov Boundary-Based Approach to Causal Structure Learning
In this paper, we propose a novel recursive constraint-based method for causal structure learning that significantly reduces the required number of CI tests compared to the existing literature.
