Tightening Causal Bounds via Covariate-Aware Optimal Transport

TL;DR

This paper introduces a covariate-aware optimal transport relaxation that tightens causal bounds, making causal inference more precise and computationally feasible by leveraging standard OT methods.

Contribution

It proposes a novel relaxation of conditional optimal transport that simplifies computation and improves the tightness of causal bounds using covariate information.

Findings

Narrower partial identification intervals achieved with the proposed method.

The relaxation is asymptotically exact as penalty increases.

The approach outperforms existing methods in simulations.

Abstract

Causal estimands can vary significantly depending on the relationship between outcomes in treatment and control groups, potentially leading to wide partial identification (PI) intervals that impede decision making. Incorporating covariates can substantially tighten these bounds, but requires determining the range of PI over probability models consistent with the joint distributions of observed covariates and outcomes in treatment and control groups. This problem is known to be equivalent to a conditional optimal transport (COT) optimization task, which is more challenging than standard optimal transport (OT) due to the additional conditioning constraints. In this work, we study a tight relaxation of COT that effectively reduces it to standard OT, leveraging its well-established computational and theoretical foundations. Our relaxation incorporates covariate information and ensures narrower PI intervals for any value of the penalty parameter, while becoming asymptotically exact as a penalty increases to infinity. This approach preserves the benefits of covariate adjustment in PI and results in a data-driven estimator for the PI set that is easy to implement using existing OT packages. We analyze the convergence rate of our estimator and demonstrate the effectiveness of our approach through extensive simulations, highlighting its practical use and superior performance compared to existing methods.