{"quality_controlled":"1","year":"2025","department":[{"_id":"FrLo"}],"scopus_import":"1","OA_place":"repository","volume":258,"date_published":"2025-05-01T00:00:00Z","type":"conference","abstract":[{"text":"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.","lang":"eng"}],"_id":"20303","date_updated":"2025-09-09T07:47:13Z","oa":1,"publication_status":"published","month":"05","publication":"The 28th International Conference on Artificial Intelligence and Statistics","intvolume":"       258","language":[{"iso":"eng"}],"citation":{"apa":"Huang, S., Pfister, N., &#38; Bowden, J. (2025). Sparse causal effect estimation using two-sample summary statistics in the presence of unmeasured confounding. In <i>The 28th International Conference on Artificial Intelligence and Statistics</i> (Vol. 258, pp. 3394–3402). Mai Khao, Thailand: ML Research Press.","ieee":"S. Huang, N. Pfister, and J. Bowden, “Sparse causal effect estimation using two-sample summary statistics in the presence of unmeasured confounding,” in <i>The 28th International Conference on Artificial Intelligence and Statistics</i>, Mai Khao, Thailand, 2025, vol. 258, pp. 3394–3402.","short":"S. Huang, N. Pfister, J. Bowden, in:, The 28th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2025, pp. 3394–3402.","mla":"Huang, Shimeng, et al. “Sparse Causal Effect Estimation Using Two-Sample Summary Statistics in the Presence of Unmeasured Confounding.” <i>The 28th International Conference on Artificial Intelligence and Statistics</i>, vol. 258, ML Research Press, 2025, pp. 3394–402.","ama":"Huang S, Pfister N, Bowden J. Sparse causal effect estimation using two-sample summary statistics in the presence of unmeasured confounding. In: <i>The 28th International Conference on Artificial Intelligence and Statistics</i>. Vol 258. ML Research Press; 2025:3394-3402.","chicago":"Huang, Shimeng, Niklas Pfister, and Jack Bowden. “Sparse Causal Effect Estimation Using Two-Sample Summary Statistics in the Presence of Unmeasured Confounding.” In <i>The 28th International Conference on Artificial Intelligence and Statistics</i>, 258:3394–3402. ML Research Press, 2025.","ista":"Huang S, Pfister N, Bowden J. 2025. Sparse causal effect estimation using two-sample summary statistics in the presence of unmeasured confounding. The 28th International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 258, 3394–3402."},"oa_version":"Preprint","title":"Sparse causal effect estimation using two-sample summary statistics in the presence of unmeasured confounding","external_id":{"arxiv":["2410.12300"]},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","OA_type":"green","author":[{"orcid":"0000-0001-6919-821X","first_name":"Shimeng","last_name":"Huang","id":"989c2a06-fb4e-11ef-a992-ab766442255b","full_name":"Huang, Shimeng"},{"full_name":"Pfister, Niklas","last_name":"Pfister","first_name":"Niklas"},{"first_name":"Jack","last_name":"Bowden","full_name":"Bowden, Jack"}],"arxiv":1,"conference":{"location":"Mai Khao, Thailand","end_date":"2025-05-05","start_date":"2025-05-03","name":"AISTATS: Conference on Artificial Intelligence and Statistics"},"day":"01","date_created":"2025-09-07T22:01:35Z","page":"3394-3402","alternative_title":["PMLR"],"publisher":"ML Research Press","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2410.12300"}],"article_processing_charge":"No","status":"public","acknowledgement":"The authors would like to thank Stephen Burgess and Ashish Patel for helpful discussions at\r\nthe start of this research project, and Anton Rask Lundborg for helpful discussions on the\r\nuniform asymptotic results. This work was partially completed during SH’s research visit at\r\nNovo Nordisk. The authors would like to thank Jesper Ferkinghoff-Borg, Kang Li and Lewis\r\nMarsh for facilitating this visit and for discussing necessary concepts and tools in statistical\r\ngenetics at an early stage. SH and NP are supported by a research grant (0069071) from Novo\r\nNordisk Fonden. JB is funded at the University of Exeter by research grant MR/X011372/1.","publication_identifier":{"eissn":["2640-3498"]}}