Instrumental Processes Using Integrated Covariances

TL;DR

This paper introduces a novel approach to instrumental variable analysis in stochastic processes using integrated covariance matrices, enabling unified treatment of discrete and continuous-time models for causal inference.

Contribution

It develops new instrumental variable methods based on integrated covariances, extending their application to a broader class of stochastic processes.

Findings

New instrumental variable estimators for stochastic processes

Unified framework for discrete and continuous-time models

Enhanced causal inference techniques in stochastic settings

Abstract

Instrumental variable methods are often used for parameter estimation in the presence of confounding. They can also be applied in stochastic processes. Instrumental variable analysis exploits moment equations to obtain estimators for causal parameters. We show that in stochastic processes one can find such moment equations using an integrated covariance matrix. This provides new instrumental variable methods, instrumental variable methods in a class of continuous-time processes as well as a unified treatment of discrete- and continuous-time processes.