Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals

TL;DR

This paper introduces a novel approach combining deep autoregressive networks with knockoff-based counterfactuals to accurately infer nonlinear causal relations in multivariate time series, outperforming existing methods.

Contribution

The paper extends Granger causality with deep probabilistic forecasting and integrates knockoff variables for better intervention handling in nonlinear causal inference.

Findings

Outperforms vector autoregressive Granger causality in nonlinear settings

Effective on both synthetic and real-world datasets

Improves causal detection accuracy in complex multivariate time series

Abstract

Estimating causal relations is vital in understanding the complex interactions in multivariate time series. Non-linear coupling of variables is one of the major challenges inaccurate estimation of cause-effect relations. In this paper, we propose to use deep autoregressive networks (DeepAR) in tandem with counterfactual analysis to infer nonlinear causal relations in multivariate time series. We extend the concept of Granger causality using probabilistic forecasting with DeepAR. Since deep networks can neither handle missing input nor out-of-distribution intervention, we propose to use the Knockoffs framework (Barberand Cand`es, 2015) for generating intervention variables and consequently counterfactual probabilistic forecasting. Knockoff samples are independent of their output given the observed variables and exchangeable with their counterpart variables without changing the underlying distribution of the data. We test our method on synthetic as well as real-world time series datasets. Overall our method outperforms the widely used vector autoregressive Granger causality and PCMCI in detecting nonlinear causal dependency in multivariate time series.