Background: CALGB 80405 is a phase III clinical trial of FOLFOX/ FOLFIRI with randomly assigned cetuximab/ bevacizumab. Novel causal machine learning approaches to the study dataset may lead to valuable insights into CRC prognosis and management of CRC progression. Methods: Using a Bayesian causal machine learning and simulation platform, we built an ensemble of network models for overall survival (OS). We used 78 baseline clinical and demographic variables for 947 patients with wild-type KRAS tumors. Causal modeling identifies the set of conditional dependencies between variables leading to outcomes. Building an ensemble of causal models estimates model uncertainty and identifies key drivers by model consensus as measured by ensemble frequency (f). Counterfactual simulations were performed on this ensemble to identify causal drivers of disease. Results: Key causal variables of OS (f> 50%) include primary tumor side (f = 85%), aspartate aminotransferase (AST) and hemoglobin (HGB) concentrations (f = 100%, 91%), and tumor sites: local primary and intra-abdominal metastases (f = 85%, 89%). Counterfactual simulations, controlling for confounders, suggested a significant causal effect of the following variables on driving OS: AST (median hazard ratio (HR) = 1.3, 75^th vs. 25^th percentile value; 10^th - 90^th percentile interval: 1.2-1.4), HGB (0.8, 0.7-0.9), primary side (1.5, 1.0-1.7; right vs. left), and tumor sites (present vs. absent): local primary (1.3, 1.0-1.5), intra-abdominal (1.4, 1.1-1.6) and liver (1.1, 1.04-1.14) metastases. AST was a stronger biomarker of OS in patients with liver metastases (1.6, n = 705) than without (1.2, n = 242). Conclusions: Primary side, AST, HGB, and tumor sites (local primary, intra-abdominal, and liver) play a central role as independent drivers/biomarkers of OS. Availability of these measures at baseline will allow better risk stratification at initiation of treatment. Clinical trial information: U10CA180821, U10CA180882.