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

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

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

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

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

Helske, S., Helske, J., & Chihaya, G. K. (2023). From sequences to variables – Rethinking the relationship between sequences and outcomes. Sociological Methodology (online first). doi:10.1177/008117502311770

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

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

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

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. (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

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., 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

Karvanen, J., Tikka, S., & Vihola, M. (2023). Simulating counterfactuals. arXiv preprint arXiv:2306.15328. doi:10.48550/arXiv.2306.15328

Valkonen, L., Tikka, S., Helske, J., & Karvanen, J. (2023). Price Optimization Combining Conjoint Data and Purchase History: A Causal Modeling Approach. arXiv preprint arXiv:2303.16660. doi:10.48550/arXiv.2303.16660

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

Helske, J. & Tikka, S. (2023). Estimating Causal Effects from Panel Data with Dynamic Multivariate Panel Models. doi: 10.31235/osf.io/mdwu5

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.


Tikka S. & Helske J. (2022). dynamite: Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data

  • The dynamite R package provides an easy-to-use interface for Bayesian inference of complex panel (time series) data comprising of multiple measurements per multiple individuals measured in time.

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.