{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Gao Z"],"funding":["NIBIB NIH HHS","National Institutes of Health","NIH HHS","National Science Foundation"],"pagination":["ujaf162"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12728347"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["81(4)"],"pubmed_abstract":["Heterogeneous treatment effect models allow us to compare treatments at subgroup levels and are becoming increasingly popular in applications such as personalized medicine, advertising, and education. Regardless of the type of responses (continuous, binary, count, survival), most causal estimands focus on the differences between the treatment and control conditional means. In this paper, we propose an alternative estimand, DINA-the DIfference in NAtural parameters-to quantify heterogeneous treatment effects motivated by exponential families and the Cox model. Despite the type of responses, DINA is both convenient and more practical for modeling the influence of covariates on the treatment effect. Additionally, we introduce a meta-algorithm for DINA, enabling practitioners to utilize powerf"],"journal":["Biometrics"],"pubmed_title":["Estimating heterogeneous treatment effects for general responses."],"pmcid":["PMC12728347"],"funding_grant_id":["DMS 2013736","5R01 EB 001988-21","R01 EB001988","IIS 1837931"],"pubmed_authors":["Hastie T","Gao Z"],"additional_accession":[]},"is_claimable":false,"name":"Estimating heterogeneous treatment effects for general responses.","description":"Heterogeneous treatment effect models allow us to compare treatments at subgroup levels and are becoming increasingly popular in applications such as personalized medicine, advertising, and education. Regardless of the type of responses (continuous, binary, count, survival), most causal estimands focus on the differences between the treatment and control conditional means. In this paper, we propose an alternative estimand, DINA-the DIfference in NAtural parameters-to quantify heterogeneous treatment effects motivated by exponential families and the Cox model. Despite the type of responses, DINA is both convenient and more practical for modeling the influence of covariates on the treatment effect. Additionally, we introduce a meta-algorithm for DINA, enabling practitioners to utilize powerf","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-05-26T20:09:28.968Z","creation":"2026-05-26T03:12:18.12Z"},"accession":"S-EPMC12728347","cross_references":{"pubmed":["41437932"],"doi":["10.1093/biomtc/ujaf162"]}}