Causal Inference Under Interference And Network Uncertainty
This addresses a key limitation in causal inference for applications like vulnerable communities where social ties are hard to measure, though it appears incremental by integrating existing techniques.
The paper tackles the problem of estimating causal effects when data units are interdependent and the network structure of these dependencies is uncertain, proposing a method that combines structure learning and interference techniques and demonstrating its utility on synthetic datasets with network dependence.
Classical causal and statistical inference methods typically assume the observed data consists of independent realizations. However, in many applications this assumption is inappropriate due to a network of dependences between units in the data. Methods for estimating causal effects have been developed in the setting where the structure of dependence between units is known exactly, but in practice there is often substantial uncertainty about the precise network structure. This is true, for example, in trial data drawn from vulnerable communities where social ties are difficult to query directly. In this paper we combine techniques from the structure learning and interference literatures in causal inference, proposing a general method for estimating causal effects under data dependence when the structure of this dependence is not known a priori. We demonstrate the utility of our method on synthetic datasets which exhibit network dependence.