Causal Network Learning from Multiple Interventions of Unknown Manipulated Targets
This addresses a challenge in causal inference for researchers dealing with experimental data where intervention targets are unspecified, but it appears incremental as it builds on existing structure learning methods.
The paper tackles the problem of learning causal network structures from multiple intervention datasets where the manipulated variables are unknown, such as in experiments with temperature or drug changes, and proposes graph-merging and data-pooling methods for large and small sample sizes, respectively, with simulations illustrating the approaches.
In this paper, we discuss structure learning of causal networks from multiple data sets obtained by external intervention experiments where we do not know what variables are manipulated. For example, the conditions in these experiments are changed by changing temperature or using drugs, but we do not know what target variables are manipulated by the external interventions. From such data sets, the structure learning becomes more difficult. For this case, we first discuss the identifiability of causal structures. Next we present a graph-merging method for learning causal networks for the case that the sample sizes are large for these interventions. Then for the case that the sample sizes of these interventions are relatively small, we propose a data-pooling method for learning causal networks in which we pool all data sets of these interventions together for the learning. Further we propose a re-sampling approach to evaluate the edges of the causal network learned by the data-pooling method. Finally we illustrate the proposed learning methods by simulations.