Tuotokset
Vertaisarvioidut tieteelliset artikkelit
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
Työpaperit
Helske, S., Erola, J., & Helske, J. (2024). Education, family complexity and heterogeneous causal effects of parental separation. doi: 10.31235/osf.io/4dk3w
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
Blogitekstit
Helske S. & Hägglund A. E. (2024) Isät vapaalla – Miten työympäristö näkyy isien perhevapaiden käytössä ja myöhemmässä palkkakehityksessä? INVEST-blogi
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). Isät perhevapailla: Kuka, miksi, milloin? INVEST-blogi. Teksti julkaistu suomeksi ja englanniksi.
Ohjelmistot
- R-paketti dynamite mahdollistaa kompleksisten paneeliaineistojen kausaalimallinnuksen Bayesmenetelmiä käyttäen.
- nhmgrid on R paketti epähomogeenisten Markov-mallien siirtymätodennäköisyyksien visualisointiin.
Tikka, S. (2022). cfid: Identification of Counterfactual Queries in Causal Models
- Paketti tarjoaa työkaluja rakenteellisten kausaalimallien kontrafaktuaalien identifiointiin ID* ja IDC* algoritmien avulla sekä kausaaligraafien ja kontrafaktuaalien määrittelemiseen.
Opinnäytteet
Ritala, A. (2024). A Bayesian two-part model for improving social assistance estimation of the SISU microsimulation model . Tilastotieteen pro gradu -tutkielma. Jyväskylän yliopisto.
Jäntti, M. (2024). nhmgrid: R-paketti epähomogeenisten Markovin mallien todennäköisyyksien visualisointiin. Tilastotieteen pro gradu -tutkielma. Jyväskylän yliopisto.
Akter, S. (2023). Impact of Parental Leave Reform on Fertility in Finland. Pro gradu -tutkielma Eriarvoisuuden, interventioiden ja hyvinvointivaltion tutkimuksen maisteriohjelmaan. Turun yliopisto.