Causal artificial intelligence aims at developing bias-robust models that can be used to intervene on, rather than just be predictive, of risks or outcomes. However, learning interventional models from observational data, including electronic health records (EHR), is challenging due to inherent bias, e.g., protopathic, confounding, collider. When estimating the effects of treatment interventions, classical approaches like propensity score matching are often used, but they pose limitations with large feature sets, nonlinear/nonparallel treatment group assignments, and collider bias. In this work, we used data from a large EHR consortium –OneFlorida– and evaluated causal statistical/machine learning methods for determining the effect of statin treatment on the risk of Alzheimer's disease, a debated clinical research question. We introduced a combination of directed acyclic graph (DAG) learning and comparison with expert's design, with calculation of the generalized adjustment criterion (GAC), to find an optimal set of covariates for estimation of treatment effects –ameliorating collider bias. The DAG/CAC approach was assessed together with traditional propensity score matching, inverse probability weighting, virtual-twin/counterfactual random forests, and deep counterfactual networks. We showed large heterogeneity in effect estimates upon different model configurations. Our results did not exclude a protective effect of statins, where the DAG/GAC point estimate aligned with the maximum credibility estimate, although the 95% credibility interval included a null effect, warranting further studies and replication.