Causal Inference on Sequential Treatments via Tensor Completion Article Swipe
Marginal Structural Models (MSMs) are popular for causal inference of sequential treatments in longitudinal observational studies, which however are sensitive to model misspecification. To achieve flexible modeling, we envision the potential outcomes to form a three-dimensional tensor indexed by subject, time, and treatment regime and propose a tensorized history-restricted MSM (HRMSM). The semi-parametric tensor factor model allows us to leverage the underlying low-rank structure of the potential outcomes tensor and exploit the pre-treatment covariate information to recover the counterfactual outcomes. We incorporate the inverse probability of treatment weighting in the loss function for tensor completion to adjust for time-varying confounding. Theoretically, a non-asymptotic upper bound on the Frobenius norm error for the proposed estimator is provided. Empirically, simulation studies show that the proposed tensor completion approach outperforms the parametric HRMSM and existing matrix/tensor completion methods. Finally, we illustrate the practical utility of the proposed approach to study the effect of ventilation on organ dysfunction from the Medical Information Mart for Intensive Care database.
Related Topics
Concepts
Inverse probability weighting
Causal inference
Covariate
Categorical variable
Estimator
Leverage (statistics)
Inference
Mathematics
Tensor (intrinsic definition)
Counterfactual thinking
Exploit
Mathematical optimization
Matrix norm
Computer science
Weighting
Parametric statistics
Algorithm
Observational study
Upper and lower bounds
Marginal structural model
Oracle
Inverse
Artificial intelligence
Econometrics
Machine learning
Nonparametric statistics
Parametric model
Norm (philosophy)
Statistical inference
Causal model
Constraint (computer-aided design)
Data mining
Function (biology)
Applied mathematics
Imputation (statistics)
Metadata
- Type
- article
- Landing Page
- http://arxiv.org/abs/2511.15866
- https://arxiv.org/pdf/2511.15866
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7106476682
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W7106476682Canonical identifier for this work in OpenAlex
- Title
-
Causal Inference on Sequential Treatments via Tensor CompletionWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-19Full publication date if available
- Authors
-
Gao, Chenyin, Chen Han, Zhang, Anru R., Yang ShuList of authors in order
- Landing page
-
https://arxiv.org/abs/2511.15866Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2511.15866Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2511.15866Direct OA link when available
- Concepts
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Inverse probability weighting, Causal inference, Covariate, Categorical variable, Estimator, Leverage (statistics), Inference, Mathematics, Tensor (intrinsic definition), Counterfactual thinking, Exploit, Mathematical optimization, Matrix norm, Computer science, Weighting, Parametric statistics, Algorithm, Observational study, Upper and lower bounds, Marginal structural model, Oracle, Inverse, Artificial intelligence, Econometrics, Machine learning, Nonparametric statistics, Parametric model, Norm (philosophy), Statistical inference, Causal model, Constraint (computer-aided design), Data mining, Function (biology), Applied mathematics, Imputation (statistics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.causal | 7 |
| abstract_inverted_index.effect | 148 |
| abstract_inverted_index.factor | 54 |
| abstract_inverted_index.regime | 43 |
| abstract_inverted_index.tensor | 36, 53, 68, 93, 123 |
| abstract_inverted_index.Medical | 156 |
| abstract_inverted_index.achieve | 24 |
| abstract_inverted_index.exploit | 70 |
| abstract_inverted_index.however | 17 |
| abstract_inverted_index.indexed | 37 |
| abstract_inverted_index.inverse | 83 |
| abstract_inverted_index.popular | 5 |
| abstract_inverted_index.propose | 45 |
| abstract_inverted_index.recover | 76 |
| abstract_inverted_index.studies | 118 |
| abstract_inverted_index.utility | 140 |
| abstract_inverted_index.(HRMSM). | 50 |
| abstract_inverted_index.Finally, | 135 |
| abstract_inverted_index.Marginal | 0 |
| abstract_inverted_index.approach | 125, 144 |
| abstract_inverted_index.envision | 28 |
| abstract_inverted_index.existing | 131 |
| abstract_inverted_index.flexible | 25 |
| abstract_inverted_index.function | 91 |
| abstract_inverted_index.leverage | 59 |
| abstract_inverted_index.low-rank | 62 |
| abstract_inverted_index.methods. | 134 |
| abstract_inverted_index.outcomes | 31, 67 |
| abstract_inverted_index.proposed | 112, 122, 143 |
| abstract_inverted_index.studies, | 15 |
| abstract_inverted_index.subject, | 39 |
| abstract_inverted_index.Frobenius | 107 |
| abstract_inverted_index.Intensive | 160 |
| abstract_inverted_index.covariate | 73 |
| abstract_inverted_index.database. | 162 |
| abstract_inverted_index.estimator | 113 |
| abstract_inverted_index.inference | 8 |
| abstract_inverted_index.modeling, | 26 |
| abstract_inverted_index.outcomes. | 79 |
| abstract_inverted_index.potential | 30, 66 |
| abstract_inverted_index.practical | 139 |
| abstract_inverted_index.provided. | 115 |
| abstract_inverted_index.sensitive | 19 |
| abstract_inverted_index.structure | 63 |
| abstract_inverted_index.treatment | 42, 86 |
| abstract_inverted_index.weighting | 87 |
| abstract_inverted_index.Structural | 1 |
| abstract_inverted_index.completion | 94, 124, 133 |
| abstract_inverted_index.illustrate | 137 |
| abstract_inverted_index.parametric | 128 |
| abstract_inverted_index.sequential | 10 |
| abstract_inverted_index.simulation | 117 |
| abstract_inverted_index.tensorized | 47 |
| abstract_inverted_index.treatments | 11 |
| abstract_inverted_index.underlying | 61 |
| abstract_inverted_index.Information | 157 |
| abstract_inverted_index.dysfunction | 153 |
| abstract_inverted_index.incorporate | 81 |
| abstract_inverted_index.information | 74 |
| abstract_inverted_index.outperforms | 126 |
| abstract_inverted_index.probability | 84 |
| abstract_inverted_index.ventilation | 150 |
| abstract_inverted_index.Empirically, | 116 |
| abstract_inverted_index.confounding. | 99 |
| abstract_inverted_index.longitudinal | 13 |
| abstract_inverted_index.time-varying | 98 |
| abstract_inverted_index.matrix/tensor | 132 |
| abstract_inverted_index.observational | 14 |
| abstract_inverted_index.pre-treatment | 72 |
| abstract_inverted_index.Theoretically, | 100 |
| abstract_inverted_index.counterfactual | 78 |
| abstract_inverted_index.non-asymptotic | 102 |
| abstract_inverted_index.semi-parametric | 52 |
| abstract_inverted_index.misspecification. | 22 |
| abstract_inverted_index.three-dimensional | 35 |
| abstract_inverted_index.history-restricted | 48 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.88321446 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |