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Non-parametric efficient estimation of marginal structural models with multi-valued time-varying treatments

arXiv 2409.18782 ↗First posted 27 Sep 2024stat.ME stat.MLcited by 0

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Abstract (arXiv)

Marginal structural models are a popular method for estimating causal effects in the presence of time-varying exposures. In spite of their popularity, no scalable non-parametric estimator exist for marginal structural models with multi-valued and time-varying treatments. In this paper, we use machine learning together with recent developments in semiparametric efficiency theory for longitudinal studies to propose such an estimator. The proposed estimator is based on a study of the non-parametric identifying functional, including first order von-Mises expansions as well as the efficient influence function and the efficiency bound. We show conditions under which the proposed estimator is efficient, asymptotically normal, and sequentially doubly robust in the sense that it is consistent if, for each time point, either the outcome or the treatment mechanism is consistently estimated. We perform a simulation study to illustrate the properties of the estimators, and present the results of our motivating study on a COVID-19 dataset studying the impact of mobility on the cumulative number of observed cases.

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References · 15

  1. 2015Nonparametric methods for doubly robust estimation of continuous treatment effectscited by 62
  2. 2021Semiparametric counterfactual density estimationcited by 42
  3. 2021RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forestscited by 42
  4. 2017Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimationcited by 34
  5. 2019Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inferencecited by 21
  6. 2020Non-parametric causal effects based on longitudinal modified treatment policiescited by 14
  7. 2020Assumption-lean inference for generalised linear model parameterscited by 9
  8. 2017Sequential Double Robustness in Right-Censored Longitudinal Modelscited by 8
  9. 2022Fast convergence rates for dose-response estimationcited by 8
  10. 2017On the multiply robust estimation of the mean of the g-functionalcited by 6
  11. 2018A nonparametric projection-based estimator for the probability of causation, with application to water sanitation in Kenyacited by 4
  12. 2017Doubly Robust Inference for Targeted Minimum Loss Based Estimation in Randomized Trials with Missing Outcome Datacited by 3
3 more
  1. 2021Evaluating the Impact of State-Level Public Masking Mandates on New COVID-19 Cases and Deaths in the United States: A Demonstration of the Causal Roadmapcited by 3
  2. 2018Causal isotonic regressioncited by 2
  3. 2022Causal survival analysis under competing risks using longitudinal modified treatment policiescited by 2

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