Santtu Tikka

I am an Assistant Professor at University of Jyväskylä (JYU), Department of Mathematics and Statistics. My recent work is part of the PREDLIFE project and my work is closely connected to the Decision analytics utilizing causal models and multiobjective optimization (DEMO) thematic research area of JYU. I've recently joined the European Laboratory for Learning and Intelligent Systems (ELLIS).

My main research interests include causal inference, graphical models, missing data and Bayesian methods.

You can find me on Google Scholar, Research Gate and GitHub. See also our causal modeling research group.

santtu.tikka 'at' jyu.fi

https://orcid.org/0000-0003-4039-4342

Manuscripts

Tabell O., Tikka S., Karvanen J. Clustering and pruning in causal data fusion. Submitted. (arXiv)
Saqr M., Misiejuk K., Törmänen T., Kaliisa R., Tikka S., López-Pernas S. Capturing the depth and dynamics of collaborative learning with transition network analysis. Submitted.
Tikka S., Karvanen J. Monotone missing data: a blessing and a curse. Submitted. (arXiv)
Karvanen J., Tikka S. Multiple imputation and full law identifiability. Submitted. (arXiv)
Helske S., Helske J., Chapman S., Kotimäki S., Salin M., Tikka S. Heterogeneous workplace peer effects in fathers' parental leave uptake in Finland. Submitted. (SocArXiv)
Tikka S., Karvanen J. Full law identification under missing data with categorical variables. In revision. (arXiv)
Tikka S., Helske J. dynamite: An R package for dynamic multivariate panel models. (in press) (arXiv)

Peer Reviewed Scientific Articles

Tikka S., Karvanen J. (2025). Monotone missing data: a blessing and a curse. Transactions on Machine Learning Research. (published version, arXiv)
Saqr M., Misiejuk K., Törmänen T., Kaliisa R., Tikka S., López-Pernas S. (2025). Frequency transition network analysis (FTNA). In Proceedings of the 18th International Conference on Computer-Supported Collaborative Learning (CSCL 2025), 276–280. (published version)
Saqr M., López-Pernas S., Törmänen T., Kaliisa R., Misiejuk K., Tikka S. (2025). Transition network analysis: A novel framework for modeling, visualizing, and identifying the temporal patterns of learners and learning processes. In Proceedings of the 15th International Learning Analytics and Knowledge Conference (LAK '25), 351–361. (published version, arXiv)
Karvanen J., Tikka S., Vihola M. (2024). Simulating counterfactuals. Journal of Artificial Intelligence Research, 80, 835–857. (published version, arXiv)
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. (published version, arXiv)
Helske J., Tikka S. (2024). Estimating causal effects from panel data with dynamic multivariate panel models. Advances in Life Course Research, 60, 100617. (published version, SocArXiv)
Margus A., Tikka S., Karvanen J., Lindström L. (2024). Transgenerational sublethal insecticide exposure gives rise to insecticide resistance in a pest insect. Science of The Total Environment, 908, 168114. (published version, SSRN)
Tikka S. (2023). Identifying counterfactual queries with the R package cfid. The R Journal, 15(2), 330–343. (published version, arXiv)
Tikka S., Helske J., Karvanen J. (2023). Clustering and structural robustness in causal diagrams. Journal of Machine Learning Research, 24(195), 1–32. (published version, arXiv)
Rainio M., Margus A., Tikka S., Helander M., Lindström L. (2023). The effects of short-term glyphosate-based herbicide exposure on insect gene expression profiles. Journal of Insect Physiology, 146, 104503. (published version)
Tikka S., Hakanen J., Saarela M., Karvanen J. (2021). Sima – an open-source simulation framework for realistic large-scale individual-level data generation. International Journal of Microsimulation, 14(3), 27–53. (published version, arXiv)
Tikka S., Hyttinen A., Karvanen J. (2021). Causal effect identification from multiple incomplete data sources: a general search-based approach. Journal of Statistical Software, 99(5), 1–40. (published version, arXiv)
Viinikainen J., Tikka S., Laaksonen M., Jääskeläinen T., Böckerman P., Karvanen J. (2021). Body weight and premature retirement: population-based evidence from Finland. European Journal of Public Health, 31(4), 731–736. (published version)
Helske J., Tikka S., Karvanen J. (2021). Estimation of causal effects with small data in the presence of trapdoor variables. Journal of the Royal Statistical Society: Series A (Statistics in Society), 184(3), 1030–1051. (published version, arXiv)
Karvanen J., Tikka S., Hyttinen A. (2021). Do-search – a tool for causal inference and study design with multiple data sources. Epidemiology, 32(1), 111–119. (published version, arXiv)
Tikka S., Hyttinen A., Karvanen J. (2019). Identifying causal effects via context-specific independence relations. In Proceedings of the 33rd Annual Conference on Neural Information Processing Systems. (published version, arXiv)
Margus A., Piiroinen S., Lehmann P., Tikka S., Karvanen J., Lindström L. (2019). Sublethal pyrethroid insecticide exposure carries positive fitness effects over generations in a pest insect. Scientific Reports, 9(1). (published version)
Tikka S., Karvanen J. (2019). Surrogate outcomes and transportability. International Journal of Approximate Reasoning, 108, 21–37. (published version, arXiv)
Tikka S., Karvanen J. (2018). Enhancing identification of causal effects by pruning. Journal of Machine Learning Research, 18(194), 1–23. (published version, arXiv)
Tikka S., Karvanen J. (2018). Simplifying probabilistic expressions in causal inference. Journal of Machine Learning Research, 18(36), 1–30. (published version, arXiv)
Tikka S., Karvanen J. (2017). Identifying causal effects with the R package causaleffect. Journal of Statistical Software, 76(12), 1–30. (published version, arXiv)

Book Chapters

Saqr M., Misiejuk K., Tikka S., López-Pernas S. (2026). Artificial Intelligence: Using Machine Learning to Predict Students' Performance. In Advanced Learning Analytics Methods: AI, Precision and Complexity, 41–78. Springer. (publisher website, online chapter)
Saqr M., Misiejuk K., Tikka S., López-Pernas S. (2026). Artificial Intelligence: Using Machine Learning to Classify Students and Predict Low Achievers. In Advanced Learning Analytics Methods: AI, Precision and Complexity, 79–112. Springer. (publisher website, online chapter)
Saqr M., López-Pernas S., Tikka S. (2026). Mapping Relational Dynamics with Transition Network Analysis: A Primer and Tutorial. In Advanced Learning Analytics Methods: AI, Precision and Complexity, 371–411. Springer. (publisher website, online chapter)
Saqr M., López-Pernas S., Tikka S. (2026). Capturing The Breadth and Dynamics of the Temporal Processes with Frequency Transition Network Analysis: A Primer and Tutorial. In Advanced Learning Analytics Methods: AI, Precision and Complexity, 413–446. Springer. (publisher website, online chapter)
López-Pernas S., Tikka S., Saqr M. (2026). Mining Patterns and Clusters with Transition Network Analysis: A Heterogeneity Approach. In Advanced Learning Analytics Methods: AI, Precision and Complexity, 447–468. Springer. (publisher website, online chapter)
Tikka S., Kopra J., Heinäniemi M., López-Pernas S., Saqr M. (2024). Getting started with R for Education Research. In Learning analytics methods and tutorials: A practical guide using R. Springer. (publisher website, online chapter)
Kopra J., Tikka S., Heinäniemi M., López-Pernas S., Saqr M. (2024). An R Approach to Data Cleaning and Wrangling for Education Research. In Learning analytics methods and tutorials: A practical guide using R. Springer. (publisher website, online chapter)
Tikka S., Kopra J., Heinäniemi M., López-Pernas S., Saqr M. (2024). Introductory Statistics with R for Educational Researchers. In Learning analytics methods and tutorials: A practical guide using R. Springer. (publisher website, online chapter)
López-Pernas S., Misiejuk K., Tikka S., Kopra J., Heinäniemi M., Saqr M. (2024). Visualizing and Reporting Educational Data with R. In Learning analytics methods and tutorials: A practical guide using R. Springer. (publisher website, online chapter)

R Packages

tna: Transition network analysis (authors: Sonsoles López-Pernas, Santtu Tikka, Mohammed Saqr) (GitHub, CRAN)
cfid: Identifying counterfactual queries in causal models (GitHub, CRAN)
dynamite: Bayesian inference of complex panel data (authors: Santtu Tikka, Jouni Helske, rOpenSci peer reviewed) (GitHub, CRAN, Blog post)
dosearch: Causal effect identification from multiple incomplete data sources (authors: Santtu Tikka, Antti Hyttinen, Juha Karvanen) (GitHub, CRAN)
causaleffect: Deriving expressions of joint interventional distributions and transport formulas in causal models (GitHub, CRAN)
Sima: Simulation framework for large-scale data generation in the health domain (GitHub)

Theses

Tikka, S. Improving identification algorithms in causal inference, Ph.D. Thesis, University of Jyväskylä, August 2018, Accepted with honours. Received the Finnish Statistical Society outstanding doctoral thesis award 2017–2020. (JyX)
Tikka, S. Kausaalivaikutusten identifiointi algoritmisesti (in Finnish), Master's thesis, University of Jyväskylä, February 2015, Received the Leo Törnqvist award for the best master's thesis in statistics in Finland 2015–2016. (JyX)

Other works

Tikka S., López-Pernas S., Saqr M. (2025). tna: An R package for transition network analysis. Applied Psychological Measurement. (published version)