  Discovering causal effects is at the core of scientific investigation but
remains challenging when only observational data is available. In practice,
causal networks are difficult to learn and interpret, and limited to relatively
small datasets. We report a more reliable and scalable causal discovery method
(iMIIC), based on a general mutual information supremum principle, which
greatly improves the precision of inferred causal relations while
distinguishing genuine causes from putative and latent causal effects. We
showcase iMIIC on synthetic and real-life healthcare data from 396,179 breast
cancer patients from the US Surveillance, Epidemiology, and End Results
program. More than 90\% of predicted causal effects appear correct, while the
remaining unexpected direct and indirect causal effects can be interpreted in
terms of diagnostic procedures, therapeutic timing, patient preference or
socio-economic disparity. iMIIC's unique capabilities open up new avenues to
discover reliable and interpretable causal networks across a range of research
fields.
