18 citations · 33 across the 4 of their papers we have counts for
4 papers
The costs and benefits of uniformly valid causal inference with high-dimensional nuisance parameters
Niloofar Moosavi, Jenny Häggström, Xavier de Luna
Important advances have recently been achieved in developing procedures yielding uniformly valid inference for a low dimensional causal parameter when high-dimensional nuisance mod…
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…
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…
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…