Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs
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Abstract (arXiv)
The standard way to parameterize the distributions represented by a directed acyclic graph is to insert a parametric family for the conditional distribution of each random variable given its parents. We show that when one's goal is to test for or estimate an effect of a sequentially applied treatment, this natural parameterization has serious deficiencies. By reparameterizing the graph using structural nested models, these deficiencies can be avoided.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Feb 2013.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 2; 2025: 0; 2026: 1.
6 papers.
- 2022On Testability of the Front-Door Model via Verma Constraintscited by 8
- 2022On Testability and Goodness of Fit Tests in Missing Data Modelscited by 4
- 2021A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustmentcited by 3
- 2024Deep Learning Methods for the Noniterative Conditional Expectation G-Formula for Causal Inference from Complex Observational Datacited by 3
- 2024A flexible Bayesian g-formula for causal survival analyses with time-dependent confoundingcited by 1
- 2026Robust Weighted Triangulation of Causal Effects Under Model Uncertaintycited by 1
- 2026Robust Weighted Triangulation of Causal Effects Under Model Uncertainty1 Mar 2026
- 2024Deep Learning Methods for the Noniterative Conditional Expectation G-Formula for Causal Inference from Complex Observational Data28 Oct 2024
- 2024A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding4 Feb 2024
- 2022On Testability of the Front-Door Model via Verma Constraints1 Mar 2022
- 2022On Testability and Goodness of Fit Tests in Missing Data Models28 Feb 2022
- 2021A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment25 Nov 2021