I think the problem with observing interventions is it will result in impractically large sample complexity to derive the same causal conclusion as causal calcu

I think the problem with observing interventions is it will result in impractically large sample complexity to derive the same causal conclusion as causal calculus armed with assumptions of DAG structure. I think metaphysically speaking the approaches (observing interventions vs causal calc) aren’t meaningfully different in terms of inferences you can make with infinite data, see my similar observations to yours: https://vladfeinberg.com/2019/12/01/metaphysics-of-causality.html https://vladfeinberg.com/2019/12/01/metaphysics-of-causality... But if you can presume a fixed DAG you can get away with fewer observations bc then you can derive some minimal/cheap set of vars to randomize over such that the resulting experiment measures a causal effect. All causal calc does is give you a framework for clarifying assumptions necessary to derive such a set. In high dims performing randomization is exponentially costly.