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Cited by 256

Compound Treatments and Transportability of Causal Inference

Hernán, Miguel A., VanderWeele, Tyler J.·2011·Epidemiology
DOI 10.1097/ede.0b013e3182109296PMC PMC3805254
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  • Studies exploring the effects of stop amenities have typically used additive models under the assumption that the impacts of individual amenities are independent and statistically identifiable.

    “Studies exploring the effects of stop amenities have typically used additive models (Lagune-Reutler et al., 2016;Fan et al., 2016;Cervero et al., 2010;Brown et al., 2006), under the assumption that the impacts of individual amenities are independent and statistically identifiable (Hernán and VanderWeele, 2011).”

    From Does improving stop amenities help increase Bus Rapid Transit ridership? Findings based on a quasi-experiment (2021) · cites this paper as Hernán and VanderWeele, 2011

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Research works printed but not linked: 15

  1. [1]Lewis D Counterfactuals 1973 Oxford Blackwell
  2. [4]Greenland S Murray CJ Salomon JA Mathers CD Causality theory for policy uses of epidemiologic measures Summary Measures of Population Health 2002 Cambridge, MA Harvard University Press/World Health Organization
  3. [5]Hernán MA Invited commentary: hypothetical interventions to define causal effects: afterthought or prerequisite? Am J Epidemiol 2005 162 618 620 16120710
  4. [6]van deer Lan MJ Haight TJ Tager IB van deer Lan et al. respond to “Hypothetical interventions to define causal effects” Am J Epidemiol 2005 162 621 622 16120710
  5. [8]Cole SR Frangakis CE The consistency statement in causal inference: A definition or an assumption? Epidemiology 2009 20 3 5 19234395
  6. [9]VanderWeele TJ Concerning the consistency assumption in causal inference Epidemiology 2009 20 880 883 19829187
  7. [10]Pearl J On the consistency rule in causal inference: an axiom, definition, assumption, or a theorem? Epidemiology 2010 21 872 875 20864888
  8. [11]VanderWeele TJ Hernán MA Causal effects and natural laws: towards a conceptualization of causal counterfactuals for non-manipulable exposures with application to the effects of race and sex Causal Inference Stat Perspect Appl In press.
  9. [12]Halloran ME Struchiner CJ Causal inference in infectious diseases Epidemiology 1995 6 142 151 7742400
  10. [15]Hudgens MG Halloran ME Towards causal inference with interference J Am Stat Assoc 2008 103 832 842 19081744 PMC2600548
  11. [18]Taubman SL Robins JM Mittleman MA Hernán MA Alternative approaches to estimating the effects of hypothetical interventions JSM Proceedings 2008, Health Policy Statistics Section Alexandria, VA American Statistical Association
  12. [19]Cain LE Robins JM Lanoy E Logan R Costagliola D Hernán MA When to start treatment? A systematic approach to the comparison of dynamic regimes using observational data Int J Biostat 2010 6 Article 18. Available at: http://www.bepress.com/ijb/vol6/iss2/18. PMC3406513 21972433
  13. [20]Robins JM Orellana L Rotnitzky A Estimation and extrapolation of optimal treatment and testing strategies Stat Med 2008 27 4678 4721 18646286
  14. [21]Hernán MA McAdams M McGrath N Lanoy E Costagliola D Observation plans in longitudinal studies with time-varying treatments Stat Methods Med Res 2009 18 27 52 19036915 PMC3878210
  15. [23]Hernán MA Cole SR Causal diagrams and measurement bias Am J Epidemiol 2009 170 959 962 19755635 PMC2765368

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Authors
Hernán, M. A., & VanderWeele, T. J.
Venue
Epidemiology
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2011-05
Volume
22
Issue
3
Pages
368-377
ISSN
1044-3983
DOI
10.1097/ede.0b013e3182109296
PMC
PMC3805254
Cited by
256
References
3
Language
en

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