18 citations · 33 across the 4 of their papers we have counts for
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stat.ME2016
Data-Driven Confounder Selection via Markov and Bayesian Networks
Jenny Häggström
To unbiasedly estimate a causal effect on an outcome unconfoundedness is often assumed. If there is sufficient knowledge on the underlying causal structure then existing confounder…
stat.ME2013★ 18 cited
Data-driven Algorithms for Dimension Reduction in Causal Inference
Emma Persson, Jenny Häggström, Ingeborg Waernbaum +1
In observational studies, the causal effect of a treatment may be confounded with variables that are related to both the treatment and the outcome of interest. In order to identify…
stat.ME2013★ 6 cited
Targeted smoothing parameter selection for estimating average causal effects
Jenny Häggström, Xavier de Luna
The non-parametric estimation of average causal effects in observational studies often relies on controlling for confounding covariates through smoothing regression methods such as…