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Found 4 papers, 3 papers with code
Towards Causal Representations of Climate Model Data
Climate models, such as Earth system models (ESMs), are crucial for simulating future climate change based on projected Shared Socioeconomic Pathways (SSP) greenhouse gas emissions scenarios.
Typing assumptions improve identification in causal discovery
Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an equivalence class.
Differentiable Causal Discovery from Interventional Data
This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interventional data.
Gradient-Based Neural DAG Learning
We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data.
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