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Review of methods for assessing the causal effect of binary interventions from aggregate time-series observational data

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Review of methods for assessing the causal effect of binary interventions from aggregate time-series observational data. / Samartsidis, Pantelis; Seaman, Shaun R.; Presanis, Anne M; Hickman, Matthew; De Angelis, Daniela.

In: Statistical Science, 19.05.2019.

Research output: Contribution to journalArticle

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APA

Samartsidis, P., Seaman, S. R., Presanis, A. M., Hickman, M., & De Angelis, D. (Accepted/In press). Review of methods for assessing the causal effect of binary interventions from aggregate time-series observational data. Statistical Science.

Vancouver

Samartsidis P, Seaman SR, Presanis AM, Hickman M, De Angelis D. Review of methods for assessing the causal effect of binary interventions from aggregate time-series observational data. Statistical Science. 2019 May 19.

Author

Samartsidis, Pantelis ; Seaman, Shaun R. ; Presanis, Anne M ; Hickman, Matthew ; De Angelis, Daniela. / Review of methods for assessing the causal effect of binary interventions from aggregate time-series observational data. In: Statistical Science. 2019.

Bibtex

@article{7f3ea80e1f8c4ddebddf53d6a8d19cfb,
title = "Review of methods for assessing the causal effect of binary interventions from aggregate time-series observational data",
abstract = "Researchers are often challenged with assessing the impact of an intervention on an outcome of interest in situations where the intervention is non-randomised, the intervention is only applied to one or few units, the intervention is binary, and outcome measurements are available at multiple time points. In this paper, we review existing methods for causal inference in these situations.We detail the assumptions underlying each method, emphasize connections between the dierent approaches and provide guidelines regarding their practical implementation. Several open problems are identied thus highlighting the need for future research.",
keywords = "intervention evaluation, panel data",
author = "Pantelis Samartsidis and Seaman, {Shaun R.} and Presanis, {Anne M} and Matthew Hickman and {De Angelis}, Daniela",
year = "2019",
month = "5",
day = "19",
language = "English",
journal = "Statistical Science",
issn = "0883-4237",
publisher = "Institute of Mathematical Statistics",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - Review of methods for assessing the causal effect of binary interventions from aggregate time-series observational data

AU - Samartsidis, Pantelis

AU - Seaman, Shaun R.

AU - Presanis, Anne M

AU - Hickman, Matthew

AU - De Angelis, Daniela

PY - 2019/5/19

Y1 - 2019/5/19

N2 - Researchers are often challenged with assessing the impact of an intervention on an outcome of interest in situations where the intervention is non-randomised, the intervention is only applied to one or few units, the intervention is binary, and outcome measurements are available at multiple time points. In this paper, we review existing methods for causal inference in these situations.We detail the assumptions underlying each method, emphasize connections between the dierent approaches and provide guidelines regarding their practical implementation. Several open problems are identied thus highlighting the need for future research.

AB - Researchers are often challenged with assessing the impact of an intervention on an outcome of interest in situations where the intervention is non-randomised, the intervention is only applied to one or few units, the intervention is binary, and outcome measurements are available at multiple time points. In this paper, we review existing methods for causal inference in these situations.We detail the assumptions underlying each method, emphasize connections between the dierent approaches and provide guidelines regarding their practical implementation. Several open problems are identied thus highlighting the need for future research.

KW - intervention evaluation

KW - panel data

UR - https://arxiv.org/abs/1804.07683

M3 - Article

JO - Statistical Science

T2 - Statistical Science

JF - Statistical Science

SN - 0883-4237

ER -