Are we ready for causal discovery in biological systems using deep learning?

The field of causal discovery has advanced considerably over the past three decades, in terms of perspectives, computational methods, and foundational concepts. Nevertheless, their application to biological systems that are commonly found in nature (i.e. large-scale, self-regulating), continues to face significant challenges. In this regard, we highlight emerging approaches that go beyond the traditional assumption of global acyclicity, instead leveraging efficient and scalable neural methods to infer pairwise causal relationships, directly from the data. Nonetheless, there remains five key technological hurdles, which must be overcome, to realize the deeper understanding and stronger inference biological causal networks promise.