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Found 9 papers, 5 papers with code
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
We leverage this procedure and evaluate the performance of GnIES on synthetic, real, and semi-synthetic data sets.
Think before you act: A simple baseline for compositional generalization
Contrarily to humans who have the ability to recombine familiar expressions to create novel ones, modern neural networks struggle to do so.
Active Invariant Causal Prediction: Experiment Selection through Stability
A fundamental difficulty of causal learning is that causal models can generally not be fully identified based on observational data only.
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
This work provides theoretical and empirical evidence that invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations (spatial robustness).
Grouping-By-ID: Guarding Against Adversarial Domain Shifts
If two or more samples share the same class and identifier, (Y, ID)=(y, i), then we treat those samples as counterfactuals under different style interventions on the orthogonal or style features.
Conditional Variance Penalties and Domain Shift Robustness
Our goal is to minimize a loss that is robust under changes in the distribution of these style features.
Causal Structure Learning
Causal models can be viewed as a special class of graphical models that not only represent the distribution of the observed system but also the distributions under external interventions.
Methodology
Invariant Causal Prediction for Nonlinear Models
In this work, we present and evaluate an array of methods for nonlinear and nonparametric versions of ICP for learning the causal parents of given target variables.
Methodology
Preserving Differential Privacy Between Features in Distributed Estimation
Privacy is crucial in many applications of machine learning.
