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Found 7 papers, 3 papers with code
Bayesian Intervention Optimization for Causal Discovery
We demonstrate the effectiveness of our method through various experiments.
Exploring Counterfactual Alignment Loss towards Human-centered AI
In particular, we utilize the counterfactual generation's ability for causal attribution to introduce a novel loss called the CounterFactual Alignment (CF-Align) loss.
The Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimation
To accommodate these scenarios, we consider a new setting dubbed as multiple treatments and multiple outcomes.
Causal Discovery from Subsampled Time Series with Proxy Variables
Based on these, we can leverage the proxies to remove the bias induced by the hidden variables and hence achieve identifiability.
Causal Discovery via Conditional Independence Testing with Proxy Variables
Distinguishing causal connections from correlations is important in many scenarios.
Context-LGM: Leveraging Object-Context Relation for Context-Aware Object Recognition
Finally, to implement this contextual posterior, we introduce a Transformer that takes the object's information as a reference and locates correlated contextual factors.
Which Invariance Should We Transfer? A Causal Minimax Learning Approach
When this condition fails, we surprisingly find with an example that this whole stable set, although can fully exploit stable information, is not the optimal one to transfer.
