mlsanityData to 14 Oct 2026

Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders

arXiv 1902.00450 ↗First posted 1 Feb 2019cs.LG stat.AP stat.MLcited by 75

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Abstract (arXiv)

The estimation of treatment effects is a pervasive problem in medicine. Existing methods for estimating treatment effects from longitudinal observational data assume that there are no hidden confounders, an assumption that is not testable in practice and, if it does not hold, leads to biased estimates. In this paper, we develop the Time Series Deconfounder, a method that leverages the assignment of multiple treatments over time to enable the estimation of treatment effects in the presence of multi-cause hidden confounders. The Time Series Deconfounder uses a novel recurrent neural network architecture with multitask output to build a factor model over time and infer latent variables that render the assigned treatments conditionally independent; then, it performs causal inference using these latent variables that act as substitutes for the multi-cause unobserved confounders. We provide a theoretical analysis for obtaining unbiased causal effects of time-varying exposures using the Time Series Deconfounder. Using both simulated and real data we show the effectiveness of our method in deconfounding the estimation of treatment responses over time.

Key facts. Cited by 75 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Feb 2019.

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Citing papers per year: 2021: 13; 2022: 15; 2023: 10; 2024: 19; 2025: 12; 2026: 6.

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  1. 2021Towards Causal Representation Learningcited by 209
  2. 2022When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learningcited by 65
  3. 2022Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equationscited by 59
  4. 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
  5. 2021Relating Graph Neural Networks to Structural Causal Modelscited by 41
  6. 2022Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effectscited by 32
  7. 2022Estimating average causal effects from patient trajectoriescited by 29
  8. 2021Sequential Deconfounding for Causal Inference with Unobserved Confounderscited by 24
  9. 2021Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Modelscited by 22
  10. 2022Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimationcited by 21
  11. 2022Estimating individual treatment effects under unobserved confounding using binary instrumentscited by 18
  12. 2023Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounderscited by 17
38 more
  1. 2021Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxiescited by 16
  2. 2021NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatmentscited by 14
  3. 2022Estimating counterfactual treatment outcomes over time in complex multiagent scenarioscited by 12
  4. 2023Clairvoyance: A Pipeline Toolkit for Medical Time Seriescited by 12
  5. 2024ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inferencecited by 12
  6. 2022Deep Causal Learning: Representation, Discovery and Inferencecited by 11
  7. 2022A Survey of Deep Causal Models and Their Industrial Applicationscited by 10
  8. 2023Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounderscited by 10
  9. 2025TIMING: Temporality-Aware Integrated Gradients for Time Series Explanationcited by 10
  10. 2022Deep Recurrent Modelling of Granger Causality with Latent Confoundingcited by 8
  11. 2024G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimescited by 8
  12. 2025DeCaFlow: A deconfounding causal generative modelcited by 8
  13. 2024Right on Time: Revising Time Series Models by Constraining their Explanationscited by 7
  14. 2024Causal Contrastive Learning for Counterfactual Regression Over Timecited by 7
  15. 2022Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimescited by 5
  16. 2022Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventionscited by 5
  17. 2024Two-way Deconfounder for Off-policy Evaluation in Causal Reinforcement Learningcited by 5
  18. 2021Federated Estimation of Causal Effects from Observational Datacited by 4
  19. 2024Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interferencecited by 4
  20. 2025Transformer-Based Spatial-Temporal Counterfactual Outcomes Estimationcited by 4
  21. 2025DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Seriescited by 4
  22. 2023Estimating Treatment Effects in Continuous Time with Hidden Confounderscited by 3
  23. 2023Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inferencecited by 3
  24. 2025Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inferencecited by 3
  25. 2021DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Predictioncited by 2
  26. 2023CTP:A Causal Interpretable Model for Non-Communicable Disease Progression Predictioncited by 2
  27. 2024Adaptive Experiment Design with Synthetic Controlscited by 2
  28. 2024Defining Expertise: Applications to Treatment Effect Estimationcited by 2
  29. 2024Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Datacited by 2
  30. 2024Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lenscited by 2
  31. 2024An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Seriescited by 2
  32. 2024Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correctioncited by 2
  33. 2024Training and Evaluating Causal Forecasting Models for Time-Seriescited by 2
  34. 2024Semiparametric inference for impulse response functions using double/debiased machine learningcited by 2
  35. 2025Towards Causal Market Simulatorscited by 2
  36. 2025Spatio-Temporal Hierarchical Causal Modelscited by 2
  37. 2021A Taxonomy for Inference in Causal Model Familiescited by 1
  38. 2021Causal Knowledge Guided Societal Event Forecastingcited by 1

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