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Found 7 papers, 3 papers with code
Identifying Representations for Intervention Extrapolation
In this paper, we consider the task of intervention extrapolation: predicting how interventions affect an outcome, even when those interventions are not observed at training time, and show that identifiable representations can provide an effective solution to this task even if the interventions affect the outcome non-linearly.
Model-based causal feature selection for general response types
Discovering causal relationships from observational data is a fundamental yet challenging task.
Effect-Invariant Mechanisms for Policy Generalization
A major challenge in policy learning is how to adapt efficiently to unseen environments or tasks.
Exploiting Independent Instruments: Identification and Distribution Generalization
Most of the existing estimators assume that the error term in the response $Y$ and the hidden confounders are uncorrelated with the instruments $Z$.
Invariant Policy Learning: A Causal Perspective
We adopt the concept of invariance from the causality literature and introduce the notion of policy invariance.
Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders
We propose an approach to estimate the effect of multiple simultaneous interventions in the presence of hidden confounders.
Counterfactual Mean Embeddings
In this work, we propose to model counterfactual distributions using a novel Hilbert space representation called counterfactual mean embedding (CME).
