In biology, interventions—particularly genetic ones enabled by CRISPR technologies—play a pivotal role in the study of complex systems. These interventions are instrumental in both identifying potential therapeutic targets and understanding the mechanisms of action for existing treatments. With the advancement of CRISPR and the proliferation of genome-scale analyses, the challenge shifts to navigating the vast combinatorial space of genetic interventions. Addressing this, our work concentrates on estimating the effects of pairwise genetic combinations. We introduce two novel contributions: Salt, a biologically-inspired baseline that posits the mostly additive nature of combination effects, and Peper, a deep learning model that extends Salt's additive assumption to achieve unprecedented accuracy. Our comprehensive comparison against existing state-of-the-art methods, grounded in diverse metrics, and our out-of-distribution analysis highlight the limitations of current models in realistic settings. This analysis underscores the necessity for improved modeling techniques and data acquisition strategies, paving the way for more effective exploration of genetic intervention effects.