---
res:
  bibo_abstract:
  - Observational genome-wide association studies are now widely used for causal inference
    in genetic epidemiology. To maintain privacy, such data is often only publicly
    available as summary statistics, and often studies for the endogenous covariates
    and the outcome are available separately. This has necessitated methods tailored
    to two-sample summary statistics. Current state-of-the-art methods modify linear
    instrumental variable (IV) regression---with genetic variants as instruments---to
    account for unmeasured confounding. However, since the endogenous covariates can
    be high dimensional, standard IV assumptions are generally insufficient to identify
    all causal effects simultaneously. We ensure identifiability by assuming the causal
    effects are sparse and propose a sparse causal effect two-sample IV estimator,
    spaceTSIV, adapting the spaceIV estimator by Pfister and Peters (2022) for two-sample
    summary statistics. We provide two methods, based on L0- and L1-penalization,
    respectively. We prove identifiability of the sparse causal effects in the two-sample
    setting and consistency of spaceTSIV. The performance of spaceTSIV is compared
    with existing two-sample IV methods in simulations. Finally, we showcase our methods
    using real proteomic and gene-expression data for drug-target discovery.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Shimeng
      foaf_name: Huang, Shimeng
      foaf_surname: Huang
      foaf_workInfoHomepage: http://www.librecat.org/personId=989c2a06-fb4e-11ef-a992-ab766442255b
    orcid: 0000-0001-6919-821X
  - foaf_Person:
      foaf_givenName: Niklas
      foaf_name: Pfister, Niklas
      foaf_surname: Pfister
  - foaf_Person:
      foaf_givenName: Jack
      foaf_name: Bowden, Jack
      foaf_surname: Bowden
  bibo_volume: 258
  dct_date: 2025^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2640-3498
  dct_language: eng
  dct_publisher: ML Research Press@
  dct_title: Sparse causal effect estimation using two-sample summary statistics in
    the presence of unmeasured confounding@
...