Partial Identification with Noisy Covariates: A Robust Optimization Approach

Wenshuo Guo, Mingzhang Yin, Yixin Wang, Michael Jordan

Keywords: causal inference, robust optimization, noisy covariates

TL;DR: We propose a new robust optimization approach for the partial identification of causal effects given noisy covariates, under a user-specified assumption on the noise level.

Abstract:

Causal inference from observational datasets often relies on measuring and adjusting for covariates. In practice, measurements of the
covariates can often be noisy and/or biased, or only measurements of their proxies may be available. Directly adjusting for these imperfect
measurements of the covariates can lead to biased causal estimates. Moreover, without additional assumptions, the causal effects are not
point-identifiable due to the noise in these measurements. To this end, we study the partial identification of causal effects given noisy
covariates, under a user-specified assumption on the noise level. The key observation is that we can formulate the identification of the
average treatment effects (ATE) as a robust optimization problem. This formulation leads to an efficient robust optimization algorithm that
bounds the ATE with noisy covariates. We show that this robust optimization approach can extend a wide range of causal adjustment
methods to perform partial identification, including backdoor adjustment, inverse propensity score weighting, double machine learning, and front door adjustment. Across synthetic and real datasets, we find that this approach provides ATE bounds with a higher coverage probability than existing methods.