thesis defense - computer science: elena krikun

please join the department of computer science for the upcoming thesis defense:

presenter: elena krikun

thesis title: causal discovery and treatment effect modeling in breast cancer


abstract: modeling breast cancer outcomes remains challenging because of extreme molecular heterogeneity and the inability of associative models, including those developed through traditional machine learning, to support counterfactual, intervention-based clinical reasoning. building on recent advances in causal feature selection, multiomics variable selection, and individual treatment effect estimation, this thesis proposes a hybrid pipeline within a unified computational multiomics framework that integrates high-dimensional data with causal modeling to produce interpretable precision oncology models that extend beyond risk prediction.

the proposed pipeline was developed using the tcga-brca cohort as the discovery set and validated on the independent retrospective metabric cohort to assess transportability. to address the curse of dimensionality, the framework applies markov blanket-based local causal discovery across seven data modalities and reduces more than 600,000 initial features to a sparse and stable causal core. this causal representation is then used for survival modeling (c-index = 0.8085, 5-year auc = 0.8676) and individual treatment effect (ite) estimation for chemotherapy, hormone therapy, and targeted therapy. external validation on metabric achieved a c-index of 0.7200 and a 5-year auc of 0.7639, indicating moderate but clear transportability across cohorts and assay platforms. the final causal core confirmed the integration of clinical, proteomic, and epigenetic signals, and identified a long non-coding rna as a structurally relevant driver.

the treatment-effect stage used treatment-specific arm definitions reconstructed from clinical records together with a robustness-oriented validation protocol. chemotherapy showed the strongest and most stable beneficial treatment effect, most notably in the tnbc subgroup, where treatment-effect estimates remained consistently protective across estimators and overlap-adjusted variants. hormone-therapy estimates showed a consistently protective direction in receptor-positive subgroup analyses, although the magnitude of the effect was attenuated under stricter overlap control, indicating residual confounding and limited positivity in the observational setting. targeted therapy also showed a protective direction under most evaluated techniques, but given the very small number of treated patients and partial estimator disagreement, these effect estimates should be interpreted as exploratory.


committee members:
dr. abedalrhman alkhateeb (supervisor, committee chair), dr. saad b. ahmed, dr. maysa yaseen (electrical & computer engineering)

please contact grad.compsci@lakeheadu.ca for the zoom link. everyone is welcome.