Causal Imputation via Synthetic Interventions


Consider the problem of determining the effect of a drug on a specific cell type. To answer this question, researchers traditionally need to run an experiment applying the drug of interest to that cell type. This approach is not scalable - given a large number of different actions (drugs) and a large number of different contexts (cell types), it is infeasible to run an experiment for every action-context pair. In such cases, one would ideally like to predict the result for every pair while only having to perform experiments on a small subset of pairs. This task, which we label “causal imputation”, is a generalization of the causal transportability problem. In this paper, we provide two main contributions. First, we demonstrate the efficacy of the recently introduced synthetic interventions estimator on the task of causal imputation when applied to the prominent CMAP dataset. Second, we explain the demonstrated success of this estimator by introducing a generic linear structural causal model which accounts for the interaction between cell type and drug.

The Conference on Causal Learning and Reasoning
Chandler Squires
Chandler Squires
PhD Candidate

My research interests include causal structure discovery, active learning, and causal representation learning.