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Found 3 papers, 2 papers with code
Stochastic Causal Programming for Bounding Treatment Effects
Causal effect estimation is important for many tasks in the natural and social sciences.
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice
Necessity and sufficiency are the building blocks of all successful explanations.
Causal Feature Learning for Utility-Maximizing Agents
We propose a new technique, pragmatic causal feature learning (PCFL), which extends the original CFL algorithm in useful and intuitive ways.
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