Obtaining causal estimates from observational data often requires individual and sequential adjustment for multiple sources of bias. We generalize an approach combining inverse probability of selection weighting with predictive value weighting to simultaneously adjust for all specified biases. The main steps include: i) obtaining structural causal models of bias sources, ii) solving bias models, and iii) incorporating results via imputation and/or regression weighting. We tested validity in two simulation studies (A & B) each with 1,000 bootstrap samples, N of 100,000, and true odds ratio (OR) of 2.00. Multi-bias analyses for uncontrolled confounding, exposure misclassification, and selection bias were applied (OR-Bias A = 1.46, 95% CI: 1.41, 1.50; OR-Bias B = 1.54, 95% CI: 1.48, 1.60). We obtained bias-adjusted effect estimates when applying correct bias parameters (OR-Bias-adjusted A = 2.03, 95% CI: 1.96, 2.11; OR-Bias-adjusted B = 2.01, 95% CI: 1.93, 2.10). Using incorrect bias parameters (+/- 25%) still produced effect estimates with less bias than the naive estimator. Simultaneous multi-bias analysis allows for more credible causal effect estimation.