Active Bayesian Causal Inference

Christian Toth, Lars Lorch, Christian Knoll, Andreas Krause, Franz Pernkopf, Robert Peharz, Julius von Kügelgen*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


Causal discovery and causal reasoning are classically treated as separate and consecutive tasks: one first infers the causal graph, and then uses it to estimate causal effects of interventions. However, such a two-stage approach is uneconomical, especially in terms of actively collected interventional data, since the causal query of interest may not require a fully-specified causal model. From a Bayesian perspective, it is also unnatural, since a causal query (e.g., the causal graph or some causal effect) can be viewed as a latent quantity subject to posterior inference -- other unobserved quantities that are not of direct interest (e.g., the full causal model) ought to be marginalized out in this process and contribute to our epistemic uncertainty. In this work, we propose Active Bayesian Causal Inference (ABCI), a fully-Bayesian active learning framework for integrated causal discovery and reasoning, which jointly infers a posterior over causal models and queries of interest. In our approach to ABCI, we focus on the class of causally-sufficient, nonlinear additive noise models, which we model using Gaussian processes. We sequentially design experiments that are maximally informative about our target causal query, collect the corresponding interventional data, and update our beliefs to choose the next experiment. Through simulations, we demonstrate that our approach is more data-efficient than several baselines that only focus on learning the full causal graph. This allows us to accurately learn downstream causal queries from fewer samples while providing well-calibrated uncertainty estimates for the quantities of interest.
Original languageEnglish
Title of host publicationNeurIPS 2022
Publication statusAccepted/In press - 2022
Event36th Conference on Neural Information Processing Systems: NeurIPS 2022 - Hybrider Event, United States
Duration: 29 Nov 20221 Dec 2022


Conference36th Conference on Neural Information Processing Systems
Abbreviated titleNeurIPS 2022
Country/TerritoryUnited States
CityHybrider Event
OtherThirty-sixth Conference on Neural Information Processing Systems

Fields of Expertise

  • Information, Communication & Computing


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  • Intelligent Systems

    Pernkopf, F.

    1/01/02 → …

    Project: Research area

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