126 citations · 455 across the 18 of their papers we have counts for
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Feedback-augmented Non-homogeneous Hidden Markov Models for Longitudinal Causal Inference
Jouni Helske
Hidden Markov models are widely used for modeling sequential data but typically have limited applicability in observational causal inference due to their strong conditional indepen…
Hidden Markov modelling of spatio-temporal dynamics of measles in 1750-1850 Finland
Tiia-Maria Pasanen, Jouni Helske, Tarmo Ketola
Real world spatio-temporal datasets, and phenomena related to them, are often challenging to visualise or gain a general overview of. In order to summarise information encompassed…
A modern approach to transition analysis and process mining with Markov models: A tutorial with R
Jouni Helske, Satu Helske, Mohammed Saqr +2
This chapter presents an introduction to Markovian modeling for the analysis of sequence data. Contrary to the deterministic approach seen in the previous sequence analysis chapter…
dynamite: An R Package for Dynamic Multivariate Panel Models
Santtu Tikka, Jouni Helske
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 suppor…
Estimation of causal effects with small data in the presence of trapdoor variables
Jouni Helske, Santtu Tikka, Juha Karvanen
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…