Part of Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
The standard tools of causal inference have been developed to answer simple causal queries which can be easily formalized as a small number of statistical estimands in the context of a particular structural causal model (SCM); however, scientific theories often make diffuse predictions about a large number of causal variables. This article proposes a framework for parameterizing such complex causal queries as the maximum difference in causal effects associated with two sets of causal variables that have a researcher specified probability of occurring. We term this estimand the Maximum Causal Set Effect (MCSE) and develop an estimator for it that is asymptotically consistent and conservative in finite samples under assumptions that are standard in the causal inference literature. This estimator is also asymptotically normal and amenable to the non-parametric bootstrap, facilitating classical statistical inference about this novel estimand. We compare this estimator to more common latent variable approaches and find that it can uncover larger causal effects in both real world and simulated data.