Genotypic causes of a phenotypic trait are typically determined via randomized controlled intervention experiments. Such experiments are often prohibitive with respect to durations and costs, and informative prioritization of experiments is desirable. We therefore consider predicting stable rankings of genes (covariates), according to their total causal effects on a phenotype (response), from observational data. Since causal effects are generally non-identifiable from observational data only, we use a method that can infer lower bounds for the total causal effect under some assumptions. We validated our method, which we call Causal Stability Ranking (CStaR), in two situations. First, we performed knock-out experiments with Arabidopsis thaliana according to a predicted ranking based on observational gene expression data, using flowering time as phenotype of interest. Besides several known regulators of flowering time, we found almost half of the tested top ranking mutants to have a significantly changed flowering time. Second, we compared CStaR to established regression-based methods on a gene expression dataset of Saccharomyces cerevisiae. We found that CStaR outperforms these established methods. Our method allows for efficient design and prioritization of future intervention experiments, and due to its generality it can be used for a broad spectrum of applications.