Xavier de Luna

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

Chair of the steering group of Stat4Reg Lab.

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

Member of the scientific advisory board of Statistics Sweden.

Associate Editor for scientific journals: Biometrics, Computational Statistics and Data Analysis (CSDA), and Observational Studies

Email: xavier.deluna AT umu.se

Back to my Umeå University page

Research interests and projects:

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

Fields of application: Register based research in the social and medical sciences; Robotics; Life course and inequality studies; brain imaging and cognition.

Current projects:


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

PhD students:


  • Filip Edström

  • Mohammad Ghasempour

  • Kreske Ecker


  • 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:

Selected publications:

Method papers

  • 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

  • Lee, S., Ma, M. and de Luna, X. (2022). 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

  • 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.

  • 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.

  • 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

  • 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

  • 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.

  • 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 www.bepress.com/snde/vol6/iss1/art3/.

  • 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.

Subject-matter papers

  • Kreske, E, 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


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 scientific advisory board of Statistics Sweden.

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

Associate Editor for scientific journals: Biometrics, Stat and Observational Studies

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

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

2008-2015 : Affiliated researcher at the Institute of Labour Market Policy Evaluation (IFAU), Uppsala.

2009-2011: 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.

2001-2002 : Assistant Professor at the Dept. of Statistics, Umeå University, Sweden.

2000-2002 : 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).

1997-1999: Lecturer in Statistics, Dept. of Statistical Science, University College London, UK.

1996-1997: 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.