Yuqian Zhang, Weijie Ji, Jelena Bradic
arXiv 12 Nov 2021 · Statistics — Methodology
arXiv:2111.06818 · PDF · DOI · OpenAlex · Extracted main text
Estimating dynamic treatment effects is crucial across various disciplines, providing insights into the time-dependent causal impact of interventions. However, this estimation poses challenges due to time-varying confounding, leading to potentially biased estimates. Furthermore, accurately specifying the growing number of treatment assignments and outcome models with multiple exposures appears increasingly challenging to accomplish. Double robustness, which permits model misspecification, holds great value in addressing these challenges. This paper introduces a novel "sequential model doubly robust" estimator. We develop novel moment-targeting estimates to account for confounding effects and establish that root-$N$ inference can be achieved as long as at least one nuisance model is correctly specified at each exposure time, despite the presence of high-dimensional covariates. Although the nuisance estimates themselves do not achieve root-$N$ rates, the carefully designed loss functions in our framework ensure final root-$N$ inference for the causal parameter of interest. Unlike off-the-shelf high-dimensional methods, which fail to deliver robust inference under model misspecification even within the doubly robust framework, our newly developed loss functions address this limitation effectively.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | E. Smucler, A. Rotnitzky, and J. M. Robins (2019) A unifying approach for doubly-robust $l_1$ regularized estimation of causal contrasts | 1.000 | 6 | 3 | 100% |
| 2 | A. Rotnitzky, J. Robins, and L. Babino (2017) On the multiply robust estimation of the mean of the g-functional | 1.000 | 5 | 4 | 100% |
| 3 | H. Bodory, M. Huber, and L. Lafférs (2022) Evaluating (weighted) dynamic treatment effects by double machine learning | 1.000 | 5 | 3 | 100% |
| 4 | J. Bradic, W. Ji, and Y. Zhang (2024) High-dimensional inference for dynamic treatment effects | 0.965 | 10 | 6 | 90% |
| 5 | I. Dáz, N. Williams, K. L. Hoffman, and E. J. Schenck (2023) Nonparametric causal effects based on longitudinal modified treatment policies | 0.843 | 3 | 3 | 100% |
| 6 | A. R. Luedtke, O. Sofrygin, M. J. van der Laan, and M. Carone (2017) Sequential double robustness in right-censored longitudinal models | 0.843 | 3 | 3 | 100% |
| 7 | Z. Tan (2020) Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data | 0.811 | 4 | 2 | 100% |
| 8 | V. Avagyan and S. Vansteelandt (2021) High-dimensional inference for the average treatment effect under model misspecification using penalized bias-reduced double-rob… | 0.737 | 3 | 2 | 100% |
| 9 | L. Babino, A. Rotnitzky, and J. Robins (2019) Multiple robust estimation of marginal structural mean models for unconstrained outcomes | 0.737 | 3 | 2 | 100% |
| 10 | H. Bang and J. M. Robins (2005) Doubly robust estimation in missing data and causal inference models | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 41 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Dynamic covariate balancing: estimating treatment effects over time with potential local projections | 0.511 | 2 | 1 |