Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals
This work addresses the challenge of accurate causal inference in complex, non-linear time-series data, which is important for fields like economics or climate science, though it appears incremental as it builds on existing concepts like Granger causality and knockoffs.
The paper tackled the problem of estimating nonlinear causal relations in multivariate time series by proposing a method that combines deep autoregressive networks with knockoff counterfactuals, resulting in outperforming existing methods like vector autoregressive Granger causality and PCMCI in detecting such dependencies.
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.