Zobrazeno 1 - 10
of 77
pro vyhledávání: '"Tikka, Santtu"'
Autor:
Tikka, Santtu, Karvanen, Juha
Missing data may be disastrous for the identifiability of causal and statistical estimands. In graphical missing data models, colluders are dependence structures that have a special importance for identification considerations. It has been shown that
Externí odkaz:
http://arxiv.org/abs/2402.05633
Publikováno v:
Journal of Artificial Intelligence Research 80, 835-857, 2024
Counterfactual inference considers a hypothetical intervention in a parallel world that shares some evidence with the factual world. If the evidence specifies a conditional distribution on a manifold, counterfactuals may be analytically intractable.
Externí odkaz:
http://arxiv.org/abs/2306.15328
Publikováno v:
Observational Studies, 10(1), 37-53, 2024
Pricing decisions of companies require an understanding of the causal effect of a price change on the demand. When real-life pricing experiments are infeasible, data-driven decision-making must be based on alternative data sources such as purchase hi
Externí odkaz:
http://arxiv.org/abs/2303.16660
Autor:
Tikka, Santtu, Helske, Jouni
dynamite is an R package for Bayesian inference of intensive panel (time series) data comprising multiple measurements per multiple individuals measured in time. The package supports joint modeling of multiple response variables, time-varying and tim
Externí odkaz:
http://arxiv.org/abs/2302.01607
Autor:
Tikka, Santtu
Publikováno v:
The R Journal, 15(2):330-343, 2023
In the framework of structural causal models, counterfactual queries describe events that concern multiple alternative states of the system under study. Counterfactual queries often take the form of "what if" type questions such as "would an applican
Externí odkaz:
http://arxiv.org/abs/2210.14745
Publikováno v:
Journal of Machine Learning Research, 24(195):1-32, 2023
Graphs are commonly used to represent and visualize causal relations. For a small number of variables, this approach provides a succinct and clear view of the scenario at hand. As the number of variables under study increases, the graphical approach
Externí odkaz:
http://arxiv.org/abs/2111.04513
Autor:
Helske, Jouni, Tikka, Santtu
Publikováno v:
In Advances in Life Course Research June 2024 60
Publikováno v:
In Science of the Total Environment 15 January 2024 908
Publikováno v:
33rd Conference on Neural Information Processing Systems (NeurIPS 2019)
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:
http://arxiv.org/abs/2009.09768
We propose a framework for realistic data generation and simulation of complex systems and demonstrate its capabilities in the health domain. The main use cases of the framework are predicting the development of risk factors and disease occurrence, e
Externí odkaz:
http://arxiv.org/abs/2008.13558