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Compositional Models for Estimating Causal Effects
We discover novel benefits of the compositional approach in causal inference - systematic generalization to estimate counterfactual outcomes of unseen combinations of components and improved overlap guarantees between treatment and control groups compared to the classical methods for causal effect estimation.
How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference
Methods that infer causal dependence from observational data are central to many areas of science, including medicine, economics, and the social sciences.
Structure Mapping for Transferability of Causal Models
Human beings learn causal models and constantly use them to transfer knowledge between similar environments.
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