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Targeted Maximum Likelihood Based Causal Inference: Part II

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  • van der Laan Mark J.

    (University of California – Berkeley)

Abstract

In this article, we provide a template for the practical implementation of the targeted maximum likelihood estimator for analyzing causal effects of multiple time point interventions, for which the methodology was developed and presented in Part I. In addition, the application of this template is demonstrated in two important estimation problems: estimation of the effect of individualized treatment rules based on marginal structural models for treatment rules, and the effect of a baseline treatment on survival in a randomized clinical trial in which the time till event is subject to right censoring.

Suggested Citation

  • van der Laan Mark J., 2010. "Targeted Maximum Likelihood Based Causal Inference: Part II," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-33, February.
    • Handle: RePEc:bpj:ijbist:v:6:y:2010:i:2:n:3
    DOI: 10.2202/1557-4679.1241
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    References listed on IDEAS

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    1. van der Laan Mark J. & Petersen Maya L, 2007. "Causal Effect Models for Realistic Individualized Treatment and Intention to Treat Rules," The International Journal of Biostatistics, De Gruyter, vol. 3(1), pages 1-55, March.
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    Cited by:

    1. Daniel Jacob, 2021. "CATE meets ML," Digital Finance, Springer, vol. 3(2), pages 99-148, June.
    2. Guo, Xu & Fang, Yun & Zhu, Xuehu & Xu, Wangli & Zhu, Lixing, 2018. "Semiparametric double robust and efficient estimation for mean functionals with response missing at random," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 325-339.
    3. Mireille E. Schnitzer & Erica E.M. Moodie & Mark J. van der Laan & Robert W. Platt & Marina B. Klein, 2014. "Modeling the impact of hepatitis C viral clearance on end-stage liver disease in an HIV co-infected cohort with targeted maximum likelihood estimation," Biometrics, The International Biometric Society, vol. 70(1), pages 144-152, March.
    4. Sara E Moore & Anna Decker & Alan Hubbard & Rachael A Callcut & Erin E Fox & Deborah J del Junco & John B Holcomb & Mohammad H Rahbar & Charles E Wade & Martin A Schreiber & Louis H Alarcon & Karen J , 2015. "Statistical Machines for Trauma Hospital Outcomes Research: Application to the PRospective, Observational, Multi-Center Major Trauma Transfusion (PROMMTT) Study," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-16, August.
    5. Rachael V. Phillips & Mark J. van der Laan, 2022. "Rachael V. Phillips and Mark J. van der Laan’s contribution to the Discussion of ‘Assumption‐lean inference for generalised linear model parameters’ by Vansteelandt and Dukes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 717-718, July.
    6. Brathwaite, Timothy & Walker, Joan L., 2018. "Causal inference in travel demand modeling (and the lack thereof)," Journal of choice modelling, Elsevier, vol. 26(C), pages 1-18.
    7. repec:jss:jstsof:43:i13 is not listed on IDEAS
    8. Jacob, Daniel, 2021. "CATE meets ML: Conditional average treatment effect and machine learning," IRTG 1792 Discussion Papers 2021-005, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    9. Jason Roy & Kirsten J. Lum & Bret Zeldow & Jordan D. Dworkin & Vincent Lo Re & Michael J. Daniels, 2018. "Bayesian nonparametric generative models for causal inference with missing at random covariates," Biometrics, The International Biometric Society, vol. 74(4), pages 1193-1202, December.

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