Working Papers / Work in Progress

• Testing identification in mediation and dynamic treatment models (with Martin Huber and Kevin Kloiber, arXiv:2406.13826, slides), implemented in
  • R - testmedident in causalweight package.
• Mothers’ Job Search after Childbirth and Earnings (with Bernhard Schmidpeter, (May 2025), slides)
• Locking-in or Pushing-out: The Caseworker Dilemma (with Miroslav Štefánik, IER WP, slides)
• Afraid of Automation? Choose your Training Carefully (with Zuzana Koštálová, Miroslav Štefánik, IER WP)
• Causal Mechanisms of Relative Age Effects on Adolescent Risky Behaviours (with Luca Fumarco and Francesco Principe)
• Correcting for Nonignorable Nonresponse Bias in Ordinal Observational Survey Data (with Jozef Michal Mintal and Ivan Sutoris, slides)
• Treatment Effects for Discrete Misreported Outcomes (under Endogeneity) (with Daniel Gutknecht and Giovanni Mellace)
• Identification of the average treatment effect when SUTVA is violated (with Giovanni Mellace, SDU discussion paper 3/2020)

Publications

• Sensitivity of Bounds on ATEs under Survey Non-response (with Roman Nedela, Econometrics and Statistics, 2025, 34, 1-13)
• Choosing the right workplace experience - A dynamic evaluation of three activation programmes for young job seekers in Slovakia (with Miroslav Štefánik, Journal of Labour Market Research, 2024, 16 (58), 1—22)
• Double machine learning for sample selection models (with Michela Bia and Martin Huber, Journal of Business & Economic Statistics, 2024, 42 (3), 958-969, previous WP arXiv:2012.00745, presentation: UCL by MH), implemented in
  • R - treatselDML in causalweight package,
  • R - DoubleMLSSM in DoubleML package by Petronela Jasenáková,
  • Python - DoubleMLSSM in DoubleML by Michaela Kecskésová.
• Bounds on direct and indirect effects under treatment/mediator endogeneity and outcome attrition (with Martin Huber, Econometric Reviews 2022, 41 (10), 1141—1163, previous WP arXiv:2002.05253, presentation: EEA 2020)
• Evaluating (weighted) dynamic treatment effects by double machine learning (Econometrics Journal, _2022, 25 (3), 628—648, with Hugo Bodory and Martin Huber, implemented in
  • R - dyntreatDML in causalweight package.
• Causal mediation analysis with double machine learning (Econometrics Journal, 2022, 25 (2), 277—300, with Helmut Farbmacher, Martin Huber, Henrika Langen and Martin Spindler, May 2022 Editor’s choice article, presentations: MonashU, ESWC 2020 by MH), implemented in
  • R - medDML in causalweight package.
• The Impact of Repeated Mass Antigen Testing for COVID-19 on the Prevalence of the Disease (Journal of Population Economics, 2021, 34, 1105—1040, with Martin Kahanec and Bernhard Schmidpeter, media coverage: Denník N)
• Early Child Development and Parents’ Labor Supply (Journal of Applied Econometrics, 2021, 36, (2), 190-208, IZA discussion paper 13531 with Bernhard Schmidpeter)
• Bounding Average Treatment Effects using Linear Programming (Empirical Economics, 2019, 57, (3), 727-767, view-only link, based on chapter 3 here, previous version Cemmap CWP70/15, MATLAB code)
• Identification in Models with Discrete Variables (Computational Economics, 2019, 53, (2), 657-698, view-only link, based on chapter 1 here, previous version NHH discussion paper 01/2013)
• Sharp IV Bounds on Average Treatment Effects on the Treated and other Populations under Endogeneity and Noncompliance (Journal of Applied Econometrics, 2017, 32, (1), 56-79, with Martin Huber and Giovanni Mellace, appendix, MATLAB code, “Economicus” prize awarded (VÚB foundation))
• Sensitivity of the Bounds on the ATE in the Presence of Sample Selection (Economics Letters, 2017, 158, 84-87, with Roman Nedela, MATLAB code)
• A Note on Testing Instrument Validity for the Identification of LATE (Empirical Economics, 2017, 53, (3), 1281–1286, with Giovanni Mellace, view-only link, WP version: pdf)
• A Note on Bounding Average Treatment Effects (Economics Letters, 2013, 120, (3), 424-428, MATLAB code)

Research Interests

• Econometrics
• Partial Identification
• Causal Inference
• Labor Economics

Grants

• VEGA 1/0398/23 — Causality and machine learning in econometric models (principal investigator, 2023—ongoing)
• APVV-21-0360 — Applying machine learning methods to support labour market policy making (2022—ongoing)
• COST-CA21163 — Text, functional and other high-dimensional data in econometrics: New models, methods, applications (member of MC for Slovakia)
• VEGA 1/0692/20 — Sensitivity analysis in econometric models (principal investigator, project chosen among those that achieved high significance. 2020—2022)
• APVV-17-0329 — Generating scientific information to support labour market policy making (received rating: Excellent, 2017—2021)
• VEGA 1/0843/17 - Econometric methods for identification of average treatment effects (principal investigator, project chosen among those that achieved high significance. 2017—2019)

Theses

• doc. Essays in econometrics of model uncertainty (2024, MUNI)
• PhD. Essays in Partial Identification (2014, NHH)
• Mgr. Empirical likelihood estimation of interest rate diffusion model (2009, UK)

Refereeing

• Journal of Econometrics, Journal of the Royal Statistical Society: Series C (Applied Statistics), Journal of Applied Econometrics, Oxford Bulletin of Economics and Statistics, Biometrics, Journal of Human Resources, Economics Letters, Empirical Economics, Advances in Statistical Analysis, Journal of Econometric Methods, European Journal of Operations Research, Journal of Environmental Economics and Management, Research in Statistics, Journal of Statistical Computation and Simulation, Journal of Data Science, Applied Economics, Ekonomický Časopis
• Social Policy Insitute, Institute for Healthcare Analyses, VEGA grant scheme, Riksbankens jubileumsfond