Output

Peer-reviewed scientific articles

Pasanen, T.-M., Helske, J., Högmander, H. & Ketola, T. (2024). Spatio-temporal modeling of co-dynamics of smallpox, measles and pertussis in pre-healthcare Finland. PeerJ 12:e18155. doi: 10.7717/peerj.18155

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., Helske, S., Giuliani, G. A., Chapman, S. N., & Helske, J. (2024). Adaptation to paternal leave policies in Finnish municipalities: changing gender norms and cross-border policy legacies. doi: 10.31235/osf.io/k27yw

Chapman, S. N., Kotimäki, S., Helske, S., & Hägglund, A. E. (2024). Support or suppress: Father’s parental leave uptake in the workplace context in Finland. doi: 10.31235/osf.io/9fq67

Helske, S., Helske, J., Chapman, S. N., Kotimäki, S., Salin, M., & Tikka, S. (2024). Heterogeneous workplace peer effects in fathers’ parental leave uptake in Finland. doi: 10.31235/osf.io/p3chf

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

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

Tikka S. & Helske J. (2024). dynamite: Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data

  • 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).

Jäntti, M. (2024). nhmgrid: An R package for visualization of non-homogeneous Markov model probabilities 

  • 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

Ritala, A. (2024). A Bayesian two-part model for improving social assistance estimation of the SISU microsimulation model . Master’s thesis in Statistics. University of Jyväskylä.

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.