Xavier de Luna

Professor of Statistics at Umeå School of Business, Economics and Statistics - Umeå University, Sweden

Member of the steering groups: Stat4Reg Lab,  Umeå SIMSAM Lab and  SINGS (Swedish INterdisciplinary Graduate School in register-based research). 

Editor of Scandinavian Journal of Statistics (2025-2027). Handling new submissions starting July 2024.

Associate Editor: Biometrics (2014-2024), Computational Statistics and Data Analysis (2021-2024),  and Observational Studies (2015- )

Email: xavier.deluna AT umu.se

Back to my Umeå University page

Research interests and projects:

Theory and method: Causal inference, and causal machine learning; Covariate/Model selection and dimension reduction.

Fields of application: Register based research in the social and health sciences; Life course and inequality studies. 

Current project:

• NEW: Generative AI to promote open science in register based research (Swedish Research Council, 2025-28)

• Machine learning to study causality with big datasets: towards methods yielding valid statistical conclusions (M & M Wallenberg Foundation, 2022-27) 

Teaching:

• Causal inference (MSc), Statistical inference (BSc), Machine learning (PhD program)

PhD students:

On-going:

• Natalia Andreeva 

• Filip Edström

Completed:

• Mohammad Ghasempour (PhD, 2024: Machine learning for efficient and robust causal inference and prediction)

• Kreske Ecker (PhD, 2024: Studying earnings trajectories as functional outcomes)

• Niloofar Moosavi (PhD, 2022: Valid causal inference in high-dimensional and complex settings)

• Tetiana Gorbach (Phd, 2019: Methods for longitudinal brain imaging studies with dropout)

• Philip Fowler (PhD, 2017: Methods for improving covariate balance in observational studies)

• Minna Genbäck (PhD, 2016: Uncertainty intervals and sensitivity analysis for missing data)

• Maria Josefsson (PhD, 2013: Attrition in studies of cognitive ageing)

• Mathias Lundin (PhD, 2011: Sensistivity analysis of untestable assumptions in causal inference)

• Jenny Häggström (PhD, 2011: Selection of smoothing parameters with application in causal inference)

• Suad Elezovic (PhD, 2009: Modeling financial volatility: A functional approach with applications to Swedish limit order book data)

• Ingeborg Waernbaum (PhD, 2008: Covariate selection and propensity score specification in causal inference )

Working papers:

• See recent working papers at arXiv.org

Selected publications (method followed by application papers): 

Theory and Method

• Lee, S., Ma, Y. and de Luna, X. (2025). Covariate balancing for causal inference on categorical and continuous treatment. Published online ahead of print: Econometrics and Statistics. DOI: 10.1016/j.ecosta.2022.01.007. ArXiv DOI: 2103.00527

• Ghasempour, M, Moosavi, N, de Luna, X. (2024). Convolutional neural networks for valid and efficient causal inference. Journal of Computational and Graphical Statistics, 33(2):714–723. Open access: DOI: 10.1080/10618600.2023.2257247. Available also on arXiv: 2301.11732

• Ecker, K, de Luna, X, Schelin, L (2024). Causal inference with a functional outcome. Accepted in Journal of the Royal Statistical Society: Series C, 73, 221-240. DOI (open access): 10.1093/jrsssc/qlad092.  Available also on arXiv.

• Edström, F, Hellström, T, de Luna, X (2023). Robot causal discovery aided by human interaction. Proceedings of the 32nd IEEE International Conference on Robot and Human Interactive Communication. DOI: 10.1109/RO-MAN57019.2023.10309376.

• Gorbach, T., de Luna, X., Karvanen, J., Waernbaum, I. (2023). Contrasting identifying assumptions of average causal effects: Robustness and semiparametric efficiency. Journal of Machine Learning Research, 24(197):1−65. Available on-line here. ArXiv DOI: 2111.15233

• Moosavi, N., Häggström, J. and de Luna, X. (2023). The costs and benefits of uniformly valid causal inference with high-dimensional nuisance parameters. Statistical Science, 38(1) 1-12. PDF file. DOI: 10.1214/21-STS843 ArXiv DOI:  2105.02071 

• Ghosh, T., Ma, Y. and de Luna, X. (2021). Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect. Statistica Sinica. Vol. 31, (2) : 821-842. DOI: 10.5705/ss.202018.0416. ArXiv version with Supplementary material: here. 

  • R-package SDRcausal.

• Barban, N, de Luna, X, Lundholm E, Svensson I, Billari, F (2020) Causal effects of the timing of life course events: age at retirement and subsequent health. Sociological Methods & Research, 49, 216-249. DOI:10.1177/0049124117729697. Open Science Framework: osf.io/68znv . Code available here.

• Cantoni, E. and de Luna, X. (2020). Semiparametric inference with missing data: Robustness to outliers and model misspecification. Econometrics and Statistics, 16, 108-120. DOI: 10.1016/j.ecosta.2020.01.003. ArXiv version: here.

• Genbäck, M. and de Luna, X. (2019). Causal inference accounting for unobserved confounding after outcome regression and doubly robust estimation. Biometrics 75: 506– 515. DOI: 10.1111/biom.13001. ArXiv version: here.

  • R-package for double robust and regression imputation inference.

• Lindmark, A., de Luna, X. & Eriksson, M. (2018). Sensitivity analysis for unobserved confounding of direct and indirect effects using uncertainty intervals. Statistics in Medicine  37: 1744– 1762. DOI: 10.1002/sim.7620. ArXiv: arXiv:1711.10265 

  • R-package sensmediation

• Gorbach, T. and de Luna, X. (2018). Inference for partial correlation when data are missing not at random. Statistics and Probability Letters. 141, p. 82-89. DOI: 10.1016/j.spl.2018.05.027

• de Luna, X, Fowler, P. & Johansson, P. (2017) Proxy variables and nonparametric identification of causal effects. Economics Letters, 150, 152-154. Working paper version: IZA Discussion Paper No. 10057.

• Persson, E., Häggström, J., Waernbaum, I., & de Luna, X. (2017). Data-driven algorithms for dimension reduction in causal inference. Computational Statistics & Data Analysis, 105, 280-292. Preprint. arXiv:1309.4054

  • R-package for covariate selection and causal inference.

• de Luna, X. (2016) Discussion of "Perils and potentials of self-selected entry to epidemiological studies and surveys" by N. Keiding and T.A. Louis Journal of the Royal Statistical Society, Ser A, 179(2): 319-376.

• Josefsson, M., de Luna, X., Daniels, M.J., and Nyberg, L. (2016) Causal inference with longitudinal outcomes and non-ignorable dropout: Estimating the effect of living alone on cognitive decline. Journal of the Royal Statistical Society: Series C, 65(1): 131-144. DOI: 10.1111/rssc.12110

• Häggström, J., Persson, E., Waernbaum, I., & de Luna, X. (2015). CovSel: An R Package for Covariate Selection When Estimating Average Causal Effects. Journal of Statistical Software, 68(1), 1 - 20. doi:http://dx.doi.org/10.18637/jss.v068.i01

• Genbäck, M., Stanghellini, E. and de Luna, X. (2015) Uncertainty intervals for regression parameters with non-ignorable missingness in the outcome. Statistical Papers, 56, 3, 829-847 DOI: 10.1007/s00362-014-0610-x

• de Luna, X. and Johansson, P. (2014) Testing the unconfoundedness assumption using an instrumental assumption. Journal of Causal Inference. DOI: 10.1515/jci-2013-0011

• Häggström, J. and de Luna, X. (2014) Targeted Smoothing Parameter Selection for Estimating Average Causal Effects. Computational Statistics. DOI: 10.1007/s00180-014-0515-0. ArXiv:1306.4509

• de Luna, X. and Lundin, M. (2014) Sensitivity analysis of the unconfoundedess assumption with an application to an evaluation of college choice effects on earnings. J of Applied Statistics. DOI: 10.1080/02664763.2014.890178

• de Luna, X., Waernbaum, I., and Richardson, T.  (2011)  Covariate selection for the non-parametric estimation of an average treatment effect, Biometrika, 98, 861-875. Download pdf.

  • R-package for covariate selection and causal inference.

• de Luna, X. and Johansson, P. (2010), Non-parametric inference for the effect of a treatment on survival times with application in the health and social sciences, Journal of Statistical Planning and Inference 140, 2122-2137. Paper and Erratum.

• de Luna, X. and Häggström, J. (2010), Estimating Prediction Error: Cross-Validation vs. Accumulated Prediction Error, Communications in statistics. Simulation and computation 39, 880-898.

• de Luna, X., Johansson, P., and Sjöstedt-de Luna, S. (2010) Bootstrap inference for K-nearest neighbour matching estimators. IZA Discussion Papers 5361, Institute for the Study of Labor, Bonn. Download pdf.

• de Luna, X. and Johansson, P. (2008) Graphical diagnostics of endogeneity, In Advances in Econometrics, Vol. 21: Modeling and Evaluating Treatment Effects in Econometrics, Millimet, D.L, Smith, J.A. and Vytlacil E. (Eds), pp.147-166.

• de Luna, X and Johansson, P. (2006), Exogeneity in structural equation models, Journal of Econometrics, 132, 527-543.

• Cantoni, E. and de Luna, X. (2006), Non-parametric adjustment for covariates when estimating a treatment effect, Journal of Nonparametric Statistics, 18, 227-244.

• de Luna, X. and Genton, M.G. (2005), Predictive spatio-temporal models for spatially sparse environmental data, Statistica Sinica, 15, 547-568.

• de Luna, X. and Genton, M.G. (2004), Spatio-temporal autoregressive models for US unemployment rate, in Advances in Econometrics: Spatial and Spatiotemporal Econometrics, J. P. Lesage, R. K. Pace (eds), Elsevier, Vol. 18, 283-298.

• de Luna, X. and Skouras, K. (2003), Choosing a Model Selection Strategy. Scandinavian Journal of Statistics, 30, 113-128.

• Bask, M. and de Luna, X. (2002), Characterizing the degree of stability of non-linear dynamic models, Studies in Nonlinear Dynamics and Econometrics, Vol. 6: No. 1, Article 3. Download pdf.(note: Safari is not supported for viewing this pdf file; but can be downloaded by following the link). Keyword: Lyaponov exponent. 

• de Luna, X. and Genton, M.G. (2002), Simulation-based Inference for Simultaneous Processes on Regular Lattices, Statistics and Computing, 12, 125-134.

• de Luna, X. (2001), Guaranteed-content Prediction Intervals for Non-linear Autoregressions, Journal of Forecasting, 20, 265-272.

• de Luna, X. and Genton, M.G. (2001), Robust Simulation-Based Estimation of ARMA Models. Journal of Computational and Graphical Statistics, 10, 370-387.

• de Luna, X. (2000) Prediction Intervals Based on Autoregression Forecasts, Journal of the Royal Statistical Society, Series D, 49, 87-93.

• Genton, M.G. and de Luna, X. (2000), Robust Simulation-based Estimation, Statistics and Probability Letters, 48, 253-259.

• Brännäs, K. and de Luna, X. (1998), Generalized Method of Moment and Indirect Estimation of the ARasMA Model, Computational Statistics, 13, 485-494.

• de Luna, X. (1998), An Improvement of Akaike's FPE Criterion to Reduce its Variability, Journal of Time Series Analysis, 19, 457-472.

• de Luna, X. (1998), Projected Polynomial Autoregression for Prediction of Stationary Time Series, Journal of Applied Statistics, 25, 763-776.

Applications in different fields

• Lestari, S. K., Eriksson, M,  de Luna, X., Malmberg, G. (2024) Volunteering and instrumental support during the first phase of the pandemic in Europe: the significance of COVID-19 exposure and stringent country’s COVID-19 policy. BMC Public Health, Vol. 24(1), article id 99. DOI: 10.1186/s12889-023-17507-5.

• Ecker, K, de Luna, X, Westerlund, O (2022) Regional differences in initial labour market conditions and dynamics in lifetime income trajectories. Longitudinal and life course studies, 13 (3) : 352-379. DOI: 10.1332/175795921X16427665823284. PDF available here.

• Lestari, SK, Eriksson, M, de Luna, X, Malmberg, G, and Ng, N (2022) Frailty and types of social relationships among older adults in 17 European countries: A latent class analysis. Archives of gerontology and geriatrics (Print), Vol. 101. PDF available here. 

• Lestari, SK,  de Luna, X, Eriksson, M, Malmberg, G, and Ng, N (2021) A longitudinal study on social support, social participation, and older Europeans’ Quality of life, SSM - Population Health, 13,100747, DOI: 10.1016/j.ssmph.2021.100747. 

• Gorbach, T.,  Lundquist, A.,  de Luna, X.,  Nyberg, L. and Salami, A. (2020). A hierarchical Bayesian mixture model approach for analysis of resting-state functional brain connectivity: An alternative to thresholding. Brain Connectivity. Jun 2020, 202-211. DOI: 10.1089/brain.2020.0740

• Lestari SK, de Luna X, Eriksson M, Malmberg G, Ng N. (2020) Changes in the provision of instrumental support by older adults in nine European countries during 2004-2015: a panel data analysis. BMC Geriatrics, 20(1):436. DOI: 10.1186/s12877-020-01785-4.

• Häggström, C., Garmo, H., de Luna, X., Van Hemelrijck, M., Söderkvist, K. et al. (2019) Survival after radiotherapy versus radical cystectomy for primary muscle-invasive bladder cancer: A Swedish nationwide population-based cohort study. Cancer Medicine, 8, p. 2196-2204. DOI: 10.1002/cam4.2126

• Genbäck, M. et al. (2018) Predictors of decline in self-reported health: addressing non-ignorable dropout in longitudinal studies of aging, European Journal of Ageing, 15, 211–220. DOI:  10.1007/s10433-017-0448-x 

• Gorbach, T. et al. (2017) Longitudinal association between hippocampus atrophy and episodic-memory decline, Neurobiology of Aging, 51, 167–176. DOI: 10.1016/j.neurobiolaging.2016.12.002

• Fowler, P. et al. (2017) Study protocol for the evaluation of a vocational rehabilitation, Observatonal Studies, Vol 3, 1-27. DOI: 10.1353/obs.2017.0009 

• Chaparro, P.M. et al. (2017) Childhood family structure and women's adult overweight risk: A longitudinal study, Scandinavian J of Epidemiology,  DOI: 10.1177/1403494817705997.

• Baranowska-Rataj, A., de Luna, X., Ivarsson, A. (2016) Does the number of siblings affect health in midlife?: Evidence from the Swedish Prescribed Drug Register, Demographic Research, 35: 1259-1302.

• Lindgren, U., Nilsson, K., de Luna, X., Ivarsson, A. (2016) Data Resource Profile: Swedish Microdata Research from Childhood into Lifelong Health and Welfare (Umeå SIMSAM Lab), International Journal of Epidemiology, 45(4): 1075-1075.

• Svensson, I., Lundholm, E., de Luna, X., Malmberg, G. (2015) Family Life Course and the Timing of Women's Retirement: a Sequence Analysis Approach. Population, Space and Place. PDF. DOI: 10.1002/psp.1950

• Chaparro, P.M., Ivarsson, A., Koupil, I., Nilsson, K., Häggström, J., de Luna, X., Lindgren, U. (2015) Regional inequalities in overweight and obesity among first-time pregnant women in Sweden, 1992–2010. Scandinavian Journal of Public Health, 43(5): 534-539.

• Stenberg, A., de Luna, X., Westerlund, O. (2014) Does formal education for older workers increase earnings? Evidence based on rich data and long-term follow up. Labour 28(2), 163-189. DOI: 10.1111/labr.12030

• Pudas, S., Persson J., Josefsson, M., de Luna X., Nilsson L.-G., Nyberg, L. (2013) Brain characteristics of individuals resisting age-related cognitive decline over two decades. J of Neuroscience, 33(20): 8668-8677.

• Josefsson, M., de Luna, X., Pudas, S., Nilsson, L.G., Nyberg, L. (2012) Genetic and lifestyle predictors of 15-year longitudinal change in episodic memory. J of American Geriatrics Society 60(12), 2308–2312.

• Stenberg, A., de Luna, X., and Westerlund, O. (2012) Can Adult Upper Secondary Education Delay Retirement from the Labour Market? Journal of Population Economics 25(2), 2012, 677-696. (WP version: IFAU 2010:2).

• de Luna, X., Forslund, A., and Liljeberg, L. (2008) Effekter av yrkesinriktad arbetsmarknadsutbildning f?r deltagare under perioden 2002-04, IFAU Working paper 2008:1, Institute for Labour Market Policy Evaluation (IFAU). Download pdf.

• Daunfeldt, S.-O. and de Luna, X. (2008), Central Bank Independence and Price Stability: Evidence from 23 OECD-countries, Oxford Economic Papers 60, 410-422.

• Bask, M. and de Luna, X. (2005), EMU and the Stability and Volatility of Foreign Exchange: Some Empirical Evidence, Chaos, Solitons & Fractals, 25, 737-750.

• Daunfeldt, S.-O. and de Luna, X. (2001),The Efficacy and Cost of Regime Shifts in Inflation Policies: Evidence from New Zealand and Sweden, Applied Economics, 33, 217-224.

Brief CV

Currently:

Professor at the Department of Statistics, Umeå University.

Chair of the steering groups of: the Umeå SIMSAM Lab, and of the Stat4Reg Lab.

Member of the steering group of SINGS (Swedish INterdisciplinary Graduate School in register-based research). 

Editor of Scandinavian Journal of Statistics (2025-2027).

Associate Editor: Biometrics (2014-2024), Computational Statistics and Data Analysis (2021-2024), Observational Studies (2015- ).

2011-2023: Member of the scientific advisory board of Statistics Sweden.

2014: Guest researcher at Research Center of Statistics, University of Geneva.

2011-12: Review Panel (HS-B) for grant applications at the Swedish Research Council.

2007- : Professor of Statistics, Umeå University, Sweden.

2008-15 : Affiliated researcher at the Institute of Labour Market Policy Evaluation (IFAU), Uppsala.

2009-11: Head of the Department of Statistics, Umeå University, Sweden.

2007-08 : Research Fellow at the Institute of Labour Market Policy Evaluation (IFAU), Uppsala.

2006: Guest researcher at Center for Statistics and the Social Sciences, University of Washington, Seattle.

2002-06 : Associate Professor at the Dept. of Statistics, Umeå University, Sweden.

2001-02 : Assistant Professor at the Dept. of Statistics, Umeå University, Sweden.

2000-02 : Statistical consultant at Dept. of Business Administration, Umeå University, Sweden.

1999-2001 : Research Fellow at the Dept. of Economics, Umeå University, Sweden. Research funded by the Wikström's Foundation (1999-2000), and the Wallander and Hedelius' Foundation (2000-2001).

1997-99: Lecturer in Statistics, Dept. of Statistical Science, University College London, UK.

1996-97: Post-doctoral research fellow at the Dept. of Economics, Umeå University, Sweden. Funded by the Swiss National Science Foundation.

1996: Docteur ès sciences (PhD in Statistics), Swiss Federal Institute of Technology, Lausanne, Switzerland.