Geodesic Causal Inference

TL;DR

This paper introduces a novel framework for causal inference in geodesic metric spaces, enabling treatment effect estimation for complex outcomes like compositional, network, and brain connectivity data.

Contribution

It develops a geodesic calculus and estimation methods for the geodesic average treatment effect, extending causal inference to non-scalar, structured outcomes.

Findings

Effective estimation of treatment effects in compositional data

Application to network and brain connectivity data

Demonstrated consistency and convergence of estimators

Abstract

Adjusting for confounding and imbalance when establishing statistical relationships is an increasingly important task, and causal inference methods have emerged as the most popular tool to achieve this. Causal inference has been developed mainly for scalar outcomes and recently for distributional outcomes. We introduce here a general framework for causal inference when outcomes reside in general geodesic metric spaces, where we draw on a novel geodesic calculus that facilitates scalar multiplication for geodesics and the characterization of treatment effects through the concept of the geodesic average treatment effect. Using ideas from Fr\'echet regression, we develop estimation methods of the geodesic average treatment effect and derive consistency and rates of convergence for the proposed estimators. We also study uncertainty quantification and inference for the treatment effect. Our methodology is illustrated by a simulation study and real data examples for compositional outcomes of U.S. statewise energy source data to study the effect of coal mining, network data of New York taxi trips, where the effect of the COVID-19 pandemic is of interest, and brain functional connectivity network data to study the effect of Alzheimer's disease.