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High-dimensional Inference for Dynamic Treatment Effects

Jelena Bradic, Weijie Ji, Yuqian Zhang

arXiv 10 Oct 2021 · Statistics — Methodology · publishedThe Annals of Statistics (2024) · 3 citations (OpenAlex)

arXiv:2110.04924 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Estimating dynamic treatment effects is a crucial endeavor in causal inference, particularly when confronted with high-dimensional confounders. Doubly robust (DR) approaches have emerged as promising tools for estimating treatment effects due to their flexibility. However, we showcase that the traditional DR approaches that only focus on the DR representation of the expected outcomes may fall short of delivering optimal results. In this paper, we propose a novel DR representation for intermediate conditional outcome models that leads to superior robustness guarantees. The proposed method achieves consistency even with high-dimensional confounders, as long as at least one nuisance function is appropriately parametrized for each exposure time and treatment path. Our results represent a significant step forward as they provide new robustness guarantees. The key to achieving these results is our new DR representation, which offers superior inferential performance while requiring weaker assumptions. Lastly, we confirm our findings in practice through simulations and a real data application.

Citation extraction

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appendix boundary found by appendix_command · 33% of the source is main text. Read the extracted text to check this.

Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1barticle[author] Murphy, Susan AS. A., van der Laan, Mark JM. J., Ro… (2001) )1.00073100%
2barticle[author] Bang, HeejungH. Robins, James MJ. M (2005) )1.00053100%
3barticle[author] Bradic, JelenaJ., Ji, WeijieW. Zhang, YuqianY (2023) High-dimensional inference for dynamic treatment effects self0.92844100%
4barticle[author] Babino, LuciaL., Rotnitzky, AndreaA. Robins, JamesJ (2019) )0.92843100%
5barticle[author] Bodory, HugoH., Huber, MartinM. Lafférs, LukásL (2022) )0.73732100%
6barticle[author] Chernozhukov, VictorV., Chetverikov, DenisD., Demir… (2018) )0.73732100%
7barticle[author] Lewis, GregG. Syrgkanis, VasilisV (2021) )0.73732100%
8bincollection[author] Robins, James MJ. M (2000) a)0.73732100%
9barticle[author] Farrell, Max HM. H (2015) )0.64422100%
10barticle[author] Murphy, Susan AS. A (2003) )0.64422100%

Showing the top 10 of 50 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Dynamic covariate balancing: estimating treatment effects over time with potential local projections0.51122
2Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals0.40511
3Difference-in-differences for mediation analysis using double machine learning0.40511
4When are time series predictions causal? The potential system and dynamic causal effects0.40511
5Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments0.40511