The proliferation of big data, wider access to advanced computing platforms, and the development of powerful statistical algorithms can uncover hidden anomalies in social data, previously dismissed as noise. Here, we combine causal inference techniques and abductive reasoning to identify fertility and mortality anomalies on twenty years of complete demographic data in the United States. We uncover real,“hidden” baby booms/busts and mortality spikes/dips, distinguishable from regular trend variations. We identify more than 22 and 156 fertility and mortality anomalies, totaling more than 200k and 600k anomalous births and deaths, respectively. Notable detectable mortality anomalies include the September 11 2001 terrorist attack in New York and the emergence and acceleration of the opioid epidemic in New Hampshire. Notable fertility anomalies include the “missing births” in Louisiana after Hurricane Katrina and the reduction in fertility behavior after the September 2008 stock market crash in Connecticut, amongst others. The combined causal inference and abductive reasoning approach can be readily adapted to find other, undiscovered social phenomena or to evaluate the efficacy of important public policies.