Jana Mareckova
Title
Prof. Dr.
Last Name
Mareckova
First name
Jana
Email
jana.mareckova@unisg.ch
ORCID
Phone
+41 71 224 23 42
19 results
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Item type:Publication, Shrinkage for Categorical RegressorsThis paper introduces a flexible regularization approach that reduces point estimation risk of group means stemming from e.g. categorical regressors, (quasi-)experimental data or panel data models. The loss function is penalized by adding weighted squared l2-norm differences between group location parameters and informative first stage estimates. Under quadratic loss, the penalized estimation problem has a simple interpretable closed-form solution that nests methods established in the literature on ridge regression, discretized support smoothing kernels and model averaging methods. We derive risk-optimal penalty parameters and propose a plug-in approach for estimation. The large sample properties are analyzed in an asymptotic local to zero framework by introducing a class of sequences for close and distant systems of locations that is sufficient for describing a large range of data generating processes. We provide the asymptotic distributions of the shrinkage estimators under different penalization schemes. The proposed plug-in estimator uniformly dominates the ordinary least squares estimator in terms of asymptotic risk if the number of groups is larger than three. Monte Carlo simulations reveal robust improvements over standard methods in finite samples. Real data examples of estimating time trends in a panel and a difference-in-differences study illustrate potential applications.Type:journal articleJournal:Journal of EconometricsVolume:223Issue:1Scopus© Citations 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, From Average Effects to Targeted Assignment: A Causal Machine Learning Analysis of Swiss Active Labor Market Policies(2025-09-15); ; ; ; Active labor market policies are widely used by the Swiss government, enrolling over half of all unemployed individuals. This paper evaluates the effectiveness of Swiss programs in improving employment and earnings outcomes using causal machine learning and rich administrative data on unemployed individuals in 2014 and 2015, including detailed labor market histories and other covariates. The findings for Swiss citizens and immigrants with permanent residency indicate a small positive average effect of a Temporary Wage Subsidy program on employment and earnings in the third year after program start. In contrast, Basic Courses, such as job application training, exhibit negative effects on both outcomes over the same period. No significant impacts are found for Employment Programs conducted outside the regular labor market or for Training Courses such as language or computer classes. The programs are most effective for individuals with a non-EU migration background, while Temporary Wage Subsidies also benefit those with lower educational attainment. Finally, shallow policy trees provide practical guidance for improving the targeting of program assignments.Type:conference contribution - Some of the metrics are blocked by yourconsent settings
Item type:Publication, From Average Effects to Targeted Assignment: A Causal Machine Learning Analysis of Swiss Active Labor Market Policies(2025-06-26); ; ; ; Active labor market policies are widely used by the Swiss government, enrolling over half of all unemployed individuals. This paper evaluates the effectiveness of Swiss programs in improving employment and earnings outcomes using causal machine learning and rich administrative data on unemployed individuals in 2014 and 2015, including detailed labor market histories and other covariates. The findings for Swiss citizens and immigrants with permanent residency indicate a small positive average effect of a Temporary Wage Subsidy program on employment and earnings in the third year after program start. In contrast, Basic Courses, such as job application training, exhibit negative effects on both outcomes over the same period. No significant impacts are found for Employment Programs conducted outside the regular labor market or for Training Courses such as language or computer classes. The programs are most effective for individuals with a non-EU migration background, while Temporary Wage Subsidies also benefit those with lower educational attainment. Finally, shallow policy trees provide practical guidance for improving the targeting of program assignments.Type:conference contribution - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Comprehensive Causal Machine Learning(2024-06-24); Uncovering causal effects at various levels of granularity provides substantial value to decision makers. Comprehensive machine learning approaches to causal effect estimation allow to use a single causal machine learning approach for estimation and inference of causal mean effects for all levels of granularity. Focusing on selection-on-observables, this paper compares three such approaches, the modified causal forest (mcf), the generalized random forest (grf), and double machine learning (dml). It also provides proven theoretical guarantees for the mcf and compares the theoretical properties of the approaches. The findings indicate that dml-based methods excel for average treatment effects at the population level (ATE) and group level (GATE) with few groups, when selection into treatment is not too strong. However, for finer causal heterogeneity, explicitly outcome-centred forest-based approaches are superior. The mcf has three additional benefits: (i) It is the most robust estimator in cases when dml-based approaches underperform because of substantial selectivity; (ii) it is the best estimator for GATEs when the number of groups gets larger; and (iii), it is the only estimator that is internally consistent, in the sense that low-dimensional causal ATEs and GATEs are obtained as aggregates of finer-grained causal parameters.Type:conference contribution - Some of the metrics are blocked by yourconsent settings
Item type:Publication, How well can Noncognitive Skills predict Unemployment? A Machine Learning Approach(2019-09-11); Pohlmeier, WinfriedPresentation at the German Statistical Week 2019 in TrierType:conference contribution - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Comprehensive Causal Machine Learning(2024-05-16); Uncovering causal effects at various levels of granularity provides substantial value to decision makers. Comprehensive machine learning approaches to causal effect estimation allow to use a single causal machine learning approach for estimation and inference of causal mean effects for all levels of granularity. Focusing on selection-on-observables, this paper compares three such approaches, the modified causal forest (mcf), the generalized random forest (grf), and double machine learning (dml). It also provides proven theoretical guarantees for the mcf and compares the theoretical properties of the approaches. The findings indicate that dml-based methods excel for average treatment effects at the population level (ATE) and group level (GATE) with few groups, when selection into treatment is not too strong. However, for finer causal heterogeneity, explicitly outcome-centred forest-based approaches are superior. The mcf has three additional benefits: (i) It is the most robust estimator in cases when dml-based approaches underperform because of substantial selectivity; (ii) it is the best estimator for GATEs when the number of groups gets larger; and (iii), it is the only estimator that is internally consistent, in the sense that low-dimensional causal ATEs and GATEs are obtained as aggregates of finer-grained causal parameters.Type:conference poster - Some of the metrics are blocked by yourconsent settings
Item type:Publication,