Output
Peer-reviewed scientific articles
Chapman, S. N., & Lummaa, V. (2024) Grandmother Effects Over the Finnish Demographic Transition. Evolutionary Human Sciences, 1-19. doi:10.1017/ehs.2023.36
Helske, J. & Tikka, S. (2024). Estimating Causal Effects from Panel Data with Dynamic Multivariate Panel Models. Advances in Life course Research, 60. doi: 10.1016/j.alcr.2024.100617
Hämäläinen, R., De Wever, B., Sipiläinen, K., Heilala, V., Helovuo, A., Lehesvuori, S., Järvinen, M., Helske, J. & Kärkkäinen, T. (2024). Using eye tracking to support professional learning in vision-intensive professions: A case of aviation pilots. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12814-9
Karvanen, J., Tikka, S., & Vihola, M. (2024). Simulating counterfactuals. Journal of Artificial Intelligence Research. https://doi.org/10.1613/jair.1.15579
Valkonen, L., Tikka, S., Helske, J., & Karvanen, J. (2024). Price Optimization Combining Conjoint Data and Purchase History: A Causal Modeling Approach. Observational Studies 10(1), 37-53. https://doi.org/10.1353/obs.2024.a929116.
Helske, J., Helske, S., Saqr, M., López-Pernas, S., & Murphy, K. (2023) A modern approach to transition analysis and process mining with Markov models: A tutorial with R. in Saqr, M. & López-Pernas, S. (Eds.) Learning analytics methods and tutorials. Springer. Published online first. url:https://lamethods.github.io/chapters/ch12-markov/ch12-markov.html
Helske, S., Helske, J., & Chihaya, G. K. (2023). From sequences to variables – Rethinking the relationship between sequences and outcomes. Sociological Methodology. doi:10.1177/00811750231177026
Helske, S. & Kawalerowicz, J. Citizens’ candidates? Labour market experiences and radical right-wing candidates in the 2014 Swedish municipal elections (2023). Acta Politica (online first). doi:10.1057/s41269-023-00304-8
López-Pernas, S., Saqr, M., Helske, S., & Murphy, K. (2023) Multi-channel sequence analysis in educatonal research: An introduction and tutorial with R. in Saqr, M. & López-Pernas, S. (Eds.) Learning analytics methods and tutorials. Springer. Published online first. url:https://lamethods.github.io/chapters/ch13-multichannel/ch13-multi.html
Saqr, M., López-Pernas, S., Helske, S., Durand, M., Studer, M., & Ritschard, G. (2023) Sequence Analysis in Education: Principles, Technique, and Tutorial with R. in Saqr, M. & López-Pernas, S. (Eds.) Learning analytics methods and tutorials. Springer. Published online first. url:https://lamethods.github.io/chapters/ch10-sequence-analysis/ch10-seq.html
Saqr, M., López-Pernas, S., Helske, S., & Hrastinski, S. (2023). The longitudinal association between engagement and achievement varies by time, students’ subgroups, and achievement state: A full program study. Computers & Education, 104787. doi:10.1016/j.compedu.2023.104787
Tikka, S. (2023). Identifying Counterfactual Queries with the R Package cfid. The R Journal , 15(2), 330-343. doi:10.32614/RJ-2023-053
Tikka, S., Helske, J., & Karvanen, J. (2023). Clustering and Structural Robustness in Causal Diagrams. Journal of Machine Learning Research 24 (195), 1-32. url:www.jmlr.org/papers/v24/21-1322.html
Valkonen, L., Helske, J. & Karvanen, J. (2023). Estimating the causal effect of timing on the reach of social media posts. Stat Methods Appl 32, 493–507.doi: 10.1007/s10260-022-00664-z
Helske, S., Keski-Säntti, M., Kivelä, J., Juutinen, A., Kääriälä, A., Gissler, M., Merikukka, M., & Lallukka, T. (2023). Predicting the stability of early employment with its timing and childhood social and health-related predictors: a mixture Markov model approach. Longitudinal and Life Course Studies, 14(1). doi:10.1332/175795921X16609201864155
Helske, J. (2022). Efficient Bayesian generalized linear models with time-varying coefficients: The walker package in R. SoftwareX, 18, 101016. doi: 10.1016/j.softx.2022.101016
Liao, T. F., Bolano, D., Brzinsky-Fay, C., Cornwell, B., Fasang, A. E., Helske, S., … & Studer, M. (2022). Sequence analysis: Its past, present, and future. Social Science Research, 107, 102772. doi:10.1016/j.ssresearch.2022.102772
Pasanen, T.-M., Voutilainen, M., Helske, J., & Högmander, H. (2022). A Bayesian spatio-temporal analysis of markets during the Finnish 1860s famine. Journal of the Royal Statistical Society: Series C (Applied Statistics), 1–21. doi: 10.1111/rssc.12577
Helske, J., Helske, S., Cooper, M., Ynnerman, A., & Besancon, L. (2021). Can visualization alleviate dichotomous thinking – Effects of visual representations on the cliff effect. IEEE Transactions on Visualization and Computer Graphics, 27(8). doi: 10.1109/TVCG.2021.3073466
Working papers
Pasanen, T.-M., Helske, J. & Ketola, T. (2024). Hidden Markov modelling of spatio-temporal dynamics of measles in 1750-1850 Finland. arXiv preprint arXiv:2405.16885. doi: 10.48550/arXiv.2405.16885
Pasanen, T.-M., Helske, J., Högmander, H. & Ketola, T. (2023). Spatio-temporal modeling of co-dynamics of smallpox, measles and pertussis in pre-healthcare Finland. arXiv preprint arXiv:2310.06538. doi: 10.48550/arXiv.2310.06538
Tikka, S., & Helske, J. (2023). dynamite: An R Package for Dynamic Multivariate Panel Models. arXiv preprint arXiv:2302.01607. doi: 10.48550/arXiv.2302.01607
Chapman, S.N., Kotimäki, S., & Helske, S. (2022). Meso-level contextual patterns of fathers’ family leave uptake in Finland. INVEST Working Papers 59/2022. doi:10.31235/osf.io/xn6pg
Blog posts
Helske J. (2023). Dynamite for Causal Inference from Panel Data using Dynamic Multivariate Panel Models. ROpensci tech note.
Kotimäki S., Chapman S., & Helske S. (2022). Fathers on family leaves: Who, when, and why? INVEST blog. Available in English and in Finnish.
Software
- The dynamite R package provides an tools for Bayesian inference of complex panel (time series) data comprising of multiple measurements per multiple individuals measured in time via dynamic multivariate panel models (DMPM).
- The nhmgrid R package provides an easy-to-use interface for visualization of non-homogeneous Markov model transition probabilities.
Tikka, S. (2022). cfid: Identification of Counterfactual Queries in Causal Models
- Facilitates the identification of counterfactual queries in structural causal models via the ID* and IDC* algorithms by Shpitser, I. and Pearl, J. (2007, 2008). Provides a simple interface for defining causal diagrams and counterfactual conjunctions.
Thesis
Jäntti, M. (2024). nhmgrid: R-paketti epähomogeenisten Markovin mallien todennäköisyyksien visualisointiin [R package for visualising probabilities of inhomogeneous Markov models]. Master’s thesis in Statistics. University of Jyväskylä
Akter, S. (2023). Impact of Parental Leave Reform on Fertility in Finland. Master’s thesis in Master’s programme in Inequalities, Interventions and New Welfare State. University of Turku.