mlsanityData to 7 Oct 2026

Causal Discovery using Model Invariance through Knockoff Interventions

arXiv 2207.04055 ↗First posted 8 Jul 2022cs.LG cs.AIcited by 4

Not on the map. Why?

Abstract (arXiv)

Cause-effect analysis is crucial to understand the underlying mechanism of a system. We propose to exploit model invariance through interventions on the predictors to infer causality in nonlinear multivariate systems of time series. We model nonlinear interactions in time series using DeepAR and then expose the model to different environments using Knockoffs-based interventions to test model invariance. Knockoff samples are pairwise exchangeable, in-distribution and statistically null variables generated without knowing the response. We test model invariance where we show that the distribution of the response residual does not change significantly upon interventions on non-causal predictors. We evaluate our method on real and synthetically generated time series. Overall our method outperforms other widely used causality methods, i.e, VAR Granger causality, VARLiNGAM and PCMCI+.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 18 of its 25 references that are in the map. First posted on arXiv 8 Jul 2022.

Builds on

18 of 25 references.

References · 18

  1. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  2. 2013Generating Sequences With Recurrent Neural Networkscited by 549
  3. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  4. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 408
  5. 2017Detecting causal associations in large nonlinear time series datasetscited by 240
  6. 2017Invariant Causal Prediction for Nonlinear Modelscited by 140
  7. 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 136
  8. 2016Panning for Gold: Model-X Knockoffs for High-dimensional Controlled Variable Selectioncited by 120
  9. 2014Controlling the false discovery rate via knockoffscited by 97
  10. 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 82
  11. 2017Invariant Causal Prediction for Sequential Datacited by 63
  12. 2018Deep Knockoffscited by 40
6 more
  1. 2020Active Invariant Causal Prediction: Experiment Selection through Stabilitycited by 37
  2. 2018Knockoffs for the mass: new feature importance statistics with false discovery guaranteescited by 11
  3. 2021Causal Effect Variational Autoencoder with Uniform Treatmentcited by 8
  4. 2021Variance Minimization in the Wasserstein Space for Invariant Causal Predictioncited by 5
  5. 2022Invariant Ancestry Searchcited by 5
  6. 2021Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactualscited by 3

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 2.

4 papers.

  1. 2022Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventionscited by 5
  2. 2024Deep Learning-based Group Causal Inference in Multivariate Time-seriescited by 1
  3. 2025Robust Causal Discovery in Real-World Time Series with Power-Lawscited by 0
  4. 2025Group Interventions on Deep Networks for Causal Discovery in Subsystemscited by 0