Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions

TL;DR

This paper introduces a novel method combining SCEVAE and Knockoff interventions to accurately estimate causal links in time series data affected by hidden confounders, validated on synthetic and real climate datasets.

Contribution

It proposes a new approach that leverages Knockoff variables with SCEVAE for better causal inference under hidden confounding in time series data.

Findings

Outperforms existing deconfounding methods on synthetic data

Effectively estimates causal links in real climate data

Using proxy variables improves causal estimation

Abstract

Latent variables often mask cause-effect relationships in observational data which provokes spurious links that may be misinterpreted as causal. This problem sparks great interest in the fields such as climate science and economics. We propose to estimate confounded causal links of time series using Sequential Causal Effect Variational Autoencoder (SCEVAE) while applying Knockoff interventions. Knockoff variables have the same distribution as the originals and preserve the correlation to other variables. This allows for counterfactuals that are more faithful to the observational distribution. We show the advantage of Knockoff interventions by applying SCEVAE to synthetic datasets with both linear and nonlinear causal links. Moreover, we apply SCEVAE with Knockoffs to real aerosol-cloud-climate observational time series data. We compare our results on synthetic data to those of a time series deconfounding method both with and without estimated confounders. We show that our method outperforms this benchmark by comparing both methods to the ground truth. For the real data analysis, we rely on expert knowledge of causal links and demonstrate how using suitable proxy variables improves the causal link estimation in the presence of hidden confounders.