Causal models predict average outcomes, not individual effects

Consequential decisions need to be based on causally robust predictions. Causality is usually analysed in such contexts through Rubin Causal Models, although they are based on overly strong assumptions and make unverifiable predictions. In this work, we develop a weaker framework for causality with assumptions that are more realistic and directly verifiable. We demonstrate its applicability to different inference methods such as RCTs, Machine Learning, and Exact Matching.