Estimating Treatment Effects in Continuous Time with Hidden Confounders

TL;DR

This paper introduces a novel continuous-time method leveraging neural differential equations to estimate treatment effects in the presence of hidden confounders, applicable to irregularly sampled longitudinal data.

Contribution

It extends deconfounding techniques to continuous-time settings using neural differential equations, addressing hidden confounders in longitudinal observational data.

Findings

Effective in synthetic datasets

Promising results on real-world data

Outperforms some existing methods

Abstract

Estimating treatment effects plays a crucial role in causal inference, having many real-world applications like policy analysis and decision making. Nevertheless, estimating treatment effects in the longitudinal setting in the presence of hidden confounders remains an extremely challenging problem. Recently, there is a growing body of work attempting to obtain unbiased ITE estimates from time-dynamic observational data by ignoring the possible existence of hidden confounders. Additionally, many existing works handling hidden confounders are not applicable for continuous-time settings. In this paper, we extend the line of work focusing on deconfounding in the dynamic time setting in the presence of hidden confounders. We leverage recent advancements in neural differential equations to build a latent factor model using a stochastic controlled differential equation and Lipschitz constrained convolutional operation in order to continuously incorporate information about ongoing interventions and irregularly sampled observations. Experiments on both synthetic and real-world datasets highlight the promise of continuous time methods for estimating treatment effects in the presence of hidden confounders.