3 papers
cs.LG2025
Amortized Causal Discovery with Prior-Fitted Networks
Mateusz Sypniewski, Mateusz Olko, Mateusz Gajewski +1
In recent years, differentiable penalized likelihood methods have gained popularity, optimizing the causal structure by maximizing its likelihood with respect to the data. However,…
cs.LG2025
VARSHAP: Addressing Global Dependency Problems in Explainable AI with Variance-Based Local Feature Attribution
Mateusz Gajewski, Mikołaj Morzy, Adam Karczmarz +1
Existing feature attribution methods like SHAP often suffer from global dependence, failing to capture true local model behavior. This paper introduces VARSHAP, a novel model-agnos…
cs.LG2025
Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery
Mateusz Olko, Mateusz Gajewski, Joanna Wojciechowska +3
Neural causal discovery methods have recently improved in terms of scalability and computational efficiency. However, our systematic evaluation highlights significant room for impr…