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BayesIMP: Uncertainty Quantification for Causal Data Fusion
Siu Lun Chau · Jean-Francois Ton · Javier González · Yee Teh · Dino Sejdinovic

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @

While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging. In this paper, we study the causal data fusion problem, where data arising from multiple causal graphs are combined to estimate the average treatment effect of a target variable. As data arises from multiple sources and can vary in quality and sample size, principled uncertainty quantification becomes essential. To that end, we introduce \emph{Bayesian Causal Mean Processes}, the framework which combines ideas from probabilistic integration and kernel mean embeddings to represent interventional distributions in the reproducing kernel Hilbert space, while taking into account the uncertainty within each causal graph. To demonstrate the informativeness of our uncertainty estimation, we apply our method to the Causal Bayesian Optimisation task and show improvements over state-of-the-art methods.

Author Information

Siu Lun Chau (University of Oxford)
Jean-Francois Ton (University of Oxford)
Javier González (Microsoft Research Cambridge)
Yee Teh (DeepMind)
Dino Sejdinovic (University of Oxford)

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