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Specifically, we consider patients affected by Acute Myeloid Leukemia (AML), an hematological cancer characterized by uncontrolled proliferation of hematopoietic stem cells in the bone marrow. Because AML responds poorly to chemotherapeutic treatments, the development of targeted therapies is essential to improve patients' prospects. In particular, the dataset we analyze contains the levels of proteins involved in cell cycle regulation and linked to the progression of the disease. We evaluate treatment effects within a causal framework represented by a Directed Acyclic Graph (DAG) model, whose vertices are the protein levels in the network. A major obstacle in implementing the above program is represented by individual heterogeneity. We address this issue through a Dirichlet Process (DP) mixture of Gaussian DAG\u2010models where both the graphical structure as well as the allied model parameters are regarded as uncertain. Our procedure determines a clustering structure of the units reflecting the underlying heterogeneity, and produces subject\u2010specific estimates of causal effects based on Bayesian Model Averaging (BMA). With reference to the AML dataset, we identify different effects of protein regulation among individuals; moreover, our method clusters patients into groups that exhibit only mild similarities with traditional categories based on morphological features.<\/jats:p>","DOI":"10.1002\/sim.9599","type":"journal-article","created":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T07:05:59Z","timestamp":1667286359000},"page":"15-32","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Bayesian graphical modeling for heterogeneous causal effects"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7911-2942","authenticated-orcid":false,"given":"Federico","family":"Castelletti","sequence":"first","affiliation":[{"name":"Department of Statistical Sciences Universit\u00e0 Cattolica del Sacro Cuore Milan Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guido","family":"Consonni","sequence":"additional","affiliation":[{"name":"Department of Statistical Sciences Universit\u00e0 Cattolica del Sacro Cuore Milan Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,11]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq170"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btp543"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx174"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2007-10-119438"},{"key":"e_1_2_11_6_1","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198522195.001.0001","volume-title":"Graphical Models","author":"Lauritzen SL","year":"1996"},{"key":"e_1_2_11_7_1","volume-title":"Causality: Models, Reasoning, and Inference","author":"Pearl J","year":"2000"},{"key":"e_1_2_11_8_1","first-page":"387","volume-title":"Handbook of Big Data","author":"Maathuis M","year":"2016"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1214\/13-BA811"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1214\/18-AOS1689"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1080\/03610910601096262"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1198\/10618600152627924"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0037350"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1510489113"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1319839"},{"key":"e_1_2_11_16_1","unstructured":"LeeK Bargagli\u2010StoffiFJ DominiciF.Causal rule ensemble: interpretable inference of heterogeneous treatment effects. arXiv preprint 2020."},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1214\/19-BA1195"},{"key":"e_1_2_11_18_1","unstructured":"ShpitserI ShermanE.Identification of personalized effects associated with causal pathways. 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