The Book of Why: The New Science of Cause and Effect

by Judea Pearl

To navigate these new waters, we will have to understand the ways in which orthodox statisticians have learned to address causation and the limitations of those methods. The questions we raised above, concerning the effect of interventions, including direct and indirect effects, are not part of mainstream statistics, primarily because the field’s founding fathers purged it of the language of cause and effect. But statisticians nevertheless consider it permissible to talk about causes and effects in one situation: a randomized controlled trial (RCT) in which a treatment A is randomly assigned to some individuals and not to others and the observed changes in B are then compared. Here, both orthodox statistics and causal inference agree on the meaning of the sentence “A causes B.”