Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Santtu Tikka"'
Publikováno v:
Journal of Statistical Software, Vol 99, Iss 1 (2021)
Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system. While complete g
Externí odkaz:
https://doaj.org/article/fb20f4ae0bd3465bb2a0b4b54592f0a4
Publikováno v:
Scientific Reports, Vol 9, Iss 1, Pp 1-10 (2019)
Abstract Stress tolerance and adaptation to stress are known to facilitate species invasions. Many invasive species are also pests and insecticides are used to control them, which could shape their overall tolerance to stress. It is well-known that h
Externí odkaz:
https://doaj.org/article/6cac33ec0b554e908d682667fb7e26f5
Autor:
Santtu Tikka, Juha Karvanen
Publikováno v:
Journal of Statistical Software, Vol 76, Iss 1, Pp 1-30 (2017)
Do-calculus is concerned with estimating the interventional distribution of an action from the observed joint probability distribution of the variables in a given causal structure. All identifiable causal effects can be derived using the rules of do-
Externí odkaz:
https://doaj.org/article/f0ab3a627fb646669cb9880769de32b3
Autor:
Jouni Helske, Santtu Tikka
Panel data are ubiquitous in scientific domains such as sociology and econometrics. Various modeling approaches have been presented for the analysis of such data including dynamic panel models, cross-lagged panel models, and their extensions.Existing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::26975fe7978c60dd8a5e0dce30bc2123
https://doi.org/10.31235/osf.io/mdwu5
https://doi.org/10.31235/osf.io/mdwu5
Autor:
Mikko Laaksonen, Petri Böckerman, Santtu Tikka, Juha Karvanen, Jutta Viinikainen, T. Jaaskelainen
Publikováno v:
The European Journal of Public Health
Background Health status is a principal determinant of labour market participation. In this study, we examined whether excess weight is associated with withdrawal from the labour market owing to premature retirement. Methods The analyses were based o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4c07df21d9766125559abc570b33fe7
https://pubmed.ncbi.nlm.nih.gov/34293128
https://pubmed.ncbi.nlm.nih.gov/34293128
We consider the problem of estimating causal effects of interventions from observational data when well-known back-door and front-door adjustments are not applicable. We show that when an identifiable causal effect is subject to an implicit functiona
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5142acf1157bcd9acf1e4932f760b4be
http://urn.fi/URN:NBN:fi:jyu-202105243133
http://urn.fi/URN:NBN:fi:jyu-202105243133
Publikováno v:
Journal of Statistical Software; Vol 99 (2021); 1-40
Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating system. While complete g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::647174712e67806846fbf1e15d8edc35
https://doi.org/10.18637/jss.v099.i05
https://doi.org/10.18637/jss.v099.i05
Epidemiologic evidence is based on multiple data sources including clinical trials, cohort studies, surveys, registries, and expert opinions. Merging information from different sources opens up new possibilities for the estimation of causal effects.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28bcc4a785e4ac5ccb27bc19a80cb7f5
Autor:
Juha Karvanen, Santtu Tikka
Identification of causal effects is one of the most fundamental tasks of causal inference. We consider an identifiability problem where some experimental and observational data are available but neither data alone is sufficient for the identification
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b91f4be782c7686ba2e65124bd4752d3
http://urn.fi/URN:NBN:fi:jyu-201903121836
http://urn.fi/URN:NBN:fi:jyu-201903121836
Publikováno v:
Scopus-Elsevier
Causal effect identification considers whether an interventional probability distribution can be uniquely determined from a passively observed distribution in a given causal structure. If the generating system induces context-specific independence (C
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e72f188daf2ed18be425a5e2ea68a220
http://urn.fi/URN:NBN:fi:jyu-202001141244
http://urn.fi/URN:NBN:fi:jyu-202001141244