Vera Haugen Kvisgaard

• Kvisgaard, Vera Haugen & Pensar, Johan (2025). Bayesian Estimation of Causal Effects from Observational Categorical Data. Bayesian Analysis. ISSN 1936-0975. s. 1–28. doi: 10.1214/25-BA1540. Fulltekst i vitenarkiv Vis sammendrag

  We present a Bayesian procedure for estimation of pairwise intervention effects in a high-dimensional system of categorical variables. We assume that we have observational data generated from an unknown causal Bayesian network for which there are no latent confounders. Most of the existing methods developed for this setting assume that the underlying model is linear Gaussian, including the Bayesian IDA (BIDA) method that we build upon in this work. By combining a Bayesian backdoor estimator with model averaging, we obtain a posterior over the intervention distributions of a cause-effect pair that can be expressed as a mixture over stochastic linear combinations of Dirichlet distributions. Although there is no tractable closed-form expression for the posterior density, it is straightforward to produce Monte Carlo approximations of target quantities through direct sampling, and we also derive closed-form expressions for a few selected moments. To scale up the proposed procedure, we employ Markov Chain Monte Carlo (MCMC), which also enables us to use more efficient adjustment sets compared to the current exact BIDA. Finally, we use Jensen-Shannon divergence to define a novel causal effect based on a set of intervention distributions in the general categorical setting. We compare our method to the original IDA method and existing Bayesian approaches in numerical simulations and show that BIDA performs favorably against these alternative methods in terms of producing point estimates and discovering strong effects.

• Kvisgaard, Vera Haugen & Pensar, Johan (2025). Bayesian IDA with Background Knowledge. Italian Statistical Society Series on Advances in Statistics. ISSN 3059-2135. s. 91–96. doi: 10.1007/978-3-031-96736-8_16. Fulltekst i vitenarkiv Vis sammendrag

  There have been several recent advancements in the development of causal learning methods that combine structure learning and causal inference. Inspired by the frequentist IDA method, the Bayesian IDA (BIDA) method is a scalable Bayesian approach for estimating causal effects from observational data under an unknown causal Bayesian network. Here, we extend BIDA to the setting where the causal structure is partly known.

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• Kvisgaard, Vera Haugen & Pensar, Johan (2025). Bayesian IDA with Background Knowledge. doi: https:/nva.sikt.no/registration/019928073510-ddb0c9ca-acbc-44f5-8c56-f5a5720d1092. Fulltekst i vitenarkiv Vis sammendrag

  There have been several recent advancements in the development of causal learning methods that combine structure learning and causal inference. Inspired by the frequentist IDA method, the Bayesian IDA (BIDA) method is a scalable Bayesian approach for estimating causal effects from observational data under an unknown causal Bayesian network. Here, we extend BIDA to the setting where the causal structure is partly known.

• Holden, Steinar; Dunlop, Oona Borghild; Vik, Styrk Fjærtoft; Hjort, Ingrid; Markussen, Simen & Melberg, Hans Olav [Vis alle 15 forfattere av denne artikkelen] (2022). Holden-IV (covid-19) Hovedrapport: Samfunnsøkonomiske vurderinger av smitteverntiltak. Helse- og omsorgsdepartementet. Fulltekst i vitenarkiv

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