Inferring interactions from population time series is an active and ongoing area of research. It is relevant across many biological systems—particularly in virus-microbe communities, but also in gene regulatory networks, neural networks, and ecological communities broadly. Correlation-based inference—using correlations to predict interactions—is widespread. However, it is well-known that “correlation does not imply causation.” Despite this, many studies apply correlation-based inference methods to experimental time series without first assessing the potential scope for accurate inference. Here, we find that several correlation-based inference methods fail to recover interactions within in silico virus-microbe communities, raising questions on their relevance when applied in situ .