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Found 11 papers, 9 papers with code
Evaluating Interventional Reasoning Capabilities of Large Language Models
Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system.
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning
Although disentangled representations are often said to be beneficial for downstream tasks, current empirical and theoretical understanding is limited.
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
We study the problem of model selection in causal inference, specifically for the case of conditional average treatment effect (CATE) estimation under binary treatments.
Interventional Causal Representation Learning
Can interventional data facilitate causal representation learning?
Towards efficient representation identification in supervised learning
In this work, we depart from these assumptions and ask: a) How can we get disentanglement when the auxiliary information does not provide conditional independence over the factors of variation?
The Connection between Out-of-Distribution Generalization and Privacy of ML Models
Through extensive evaluation on a synthetic dataset and image datasets like MNIST, Fashion-MNIST, and Chest X-rays, we show that a lower OOD generalization gap does not imply better robustness to MI attacks.
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions
For many kinds of interventions, such as a new advertisement, marketing intervention, or feature recommendation, it is important to target a specific subset of people for maximizing its benefits at minimum cost or potential harm.
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End
In addition, by restricting the features that can be modified for generating counterfactual examples, we find that the top-k features from LIME or SHAP are often neither necessary nor sufficient explanations of a model's prediction.
Domain Generalization using Causal Matching
In the domain generalization literature, a common objective is to learn representations independent of the domain after conditioning on the class label.
Ranked #1 on
Domain Generalization
on Rotated Fashion-MNIST
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
For explanations of ML models in critical domains such as healthcare and finance, counterfactual examples are useful for an end-user only to the extent that perturbation of feature inputs is feasible in the real world.
A Generative Framework for Zero-Shot Learning with Adversarial Domain Adaptation
Our approach is based on end-to-end learning of the class distributions of seen classes and unseen classes.
Ranked #1 on
Zero-Shot Learning
on CUB-200 - 0-Shot Learning
(using extra training data)
