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In recent years, microeconometrics experienced the ‘credibility revolution’, culminating in the 2021 Nobel prices for David Card, Josh Angrist, and Guido Imbens. This ‘revolution’ in how to do empirical work led to more reliable empirical knowledge of the causal effects of certain public policies. In parallel, computer science, and to some extent also statistics, developed powerful (so-called Machine Learning) algorithms that are very successful in prediction tasks. The new literature on Causal Machine Learning unites these developments by using algorithms originating in Machine Learning for improved causal analysis. In this non-technical overview, I review some of these approaches. Subsequently, I use an empirical example from the field of active labour market programme evaluation to showcase how Causal Machine Learning can be applied to improve the usefulness of such studies. I conclude with some considerations about shortcomings and possible future developments of these methods as well as wider implications for teaching and empirical studies.

The role of curricular activities for the formation of education, health and behavioural outcomes has been widely studied. Yet, the role of extra-curricular activities has received little attention. This study analyzes the effect of participation in sports clubs-one of the most popular extra-curricular activities among children. We use alternative datasets and flexible semi-parametric estimation methods with a specific way to use the panel dimension of the data to address selection into sports. We find positive and robust effects on children's school performance and peer relations. Crowding out of passive leisure activities can partially explain the effects.

There is a widely held belief that natural resource rents are a blessing if institutions are strong, but a curse if institutions are weak. We use data from 3,800 Sub-Saharan African districts and apply a causal forest estimator to reassess the relationship between institutions and the effects of resource rents. Consistent with this belief, we document that stronger institutions increase the positive effect of the presence of mining activities on economic development and dampen the negative effect of mining activities on conflict. In contrast, we find that the effects of higher world mineral prices on economic development and conflict in mining districts are non-linear and vary little in institutional quality.

Microwave and optical imaging methods react differently to different land surface parameters and, thus, provide highly complementary information. However, the contribution of individual features from these two domains of the electromagnetic spectrum for tree species classification is still unclear. For large-scale forest assessments, it is moreover important to better understand the domain-specific limitations of the two sensor families, such as the impact of cloudiness and low signal-to-noise-ratio, respectively. In this study, seven deciduous and five coniferous tree species of the Austrian Biosphere Reserve Wienerwald (105,000 ha) were classified using Breiman’s random forest classifier, labeled with help of forest enterprise data. In nine test cases, variations of Sentinel-1 and Sentinel-2 imagery were passed to the classifier to evaluate their respective contributions. By solely using a high number of Sentinel-2 scenes well spread over the growing season, an overall accuracy of 83.2% was achieved. With ample Sentinel-2 scenes available, the additional use of Sentinel-1 data improved the results by 0.5 percentage points. This changed when only a single Sentinel-2 scene was supposedly available. In this case, the full set of Sentinel-1-derived features increased the overall accuracy on average by 4.7 percentage points. The same level of accuracy could be obtained using three Sentinel-2 scenes spread over the vegetation period. On the other hand, the sole use of Sentinel-1 including phenological indicators and additional features derived from the time series did not yield satisfactory overall classification accuracies (55.7%), as only coniferous species were well separated.

This paper addresses microeconometric evaluation by matching methods when the programs under consideration are heterogeneous. Assuming that selection into the different subprograms and the potential outcomes are independent given observable characteristics, estimators based on different propensity scores are compared and applied to the analysis of active labor market policies in the Swiss region of Zurich. Furthermore, the issues of heterogeneous effects and aggregation are addressed. The results suggest that an approach that incorporates the possibility of having multiple programs can be an informative tool in applied work.

There is great demand for inferring causal effect heterogeneity and for open-source statistical software, which is readily available for practitioners. The mcf package is an open-source Python package that implements Modified Causal Forest (mcf), a causal machine learner. We replicate three well-known studies in the fields of epidemiology, medicine, and labor economics to demonstrate that our mcf package produces aggregate treatment effects, which align with previous results, and in addition, provides novel insights on causal effect heterogeneity. For all resolutions of treatment effects estimation, which can be identified, the mcf package provides inference. We conclude that the mcf constitutes a practical and extensive tool for a modern causal heterogeneous effects analysis.

Decision making plays a pivotal role in shaping outcomes across various disciplines, such as medicine, economics, and business. This paper provides practitioners with guidance on implementing a decision tree designed to optimise treatment assignment policies through an interpretable and non-parametric algorithm. Building upon the method proposed by Zhou, Athey, and Wager (2023), our policy tree introduces three key innovations: a different approach to policy score calculation, the incorporation of constraints, and enhanced handling of categorical and continuous variables. These innovations enable the evaluation of a broader class of policy rules, all of which can be easily obtained using a single module. We showcase the effectiveness of our policy tree in managing multiple, discrete treatments using datasets from diverse fields. Additionally, the policy tree is implemented in the open-source Python package mcf (modified causal forest), facilitating its application in both randomised and observational research settings.

Online dating emerged as a key instrument for human mating. This research investigates the effect of sports activity on human mating by exploiting a unique data set from an online dating platform. We leverage advances in causal machine learning to estimate the causal effect of sports frequency on contact chances. We find that for male users, sport on a weekly basis increases the probability of receiving a first message from another user by 50%, relative to not doing sport. For female users, we do not find evidence for such an effect. In addition, for male users, the effect increases with higher income.

Conventional mechanical joining processes are typically rigid in their tool systems and can only react to changing process and disturbance variables to a limited extent. At the same time, various industries are increasingly trending towards multi-material systems consisting of parts with varying geometric and mechanical properties. Due to the varying properties, rigid mechanical joining processes require sampling procedures and periodic changes of tool components or auxiliary joining parts. Consequently, research is focusing on versatile mechanical joining processes that allow increased control by modifying the process parameters. Two processes based on self-piercing riveting can achieve a significant increase in process influence possibilities through a multi-linear actuator as versatile self-piercing riveting (V-SPR) and a tumbling superimposed actuator as tumbling self-piercing riveting (T-SPR). Initial research into V-SPR has shown that this process can be used to achieve a higher variation in overall package thickness by adapting the rivet geometry and using multiple linear actuators. The T-SPR process also enables increased material flow control by means of targeted compression of the rivet using the tumbling actuator, thereby extending the range of joints that can be manufactured. Based on these two processes, a combination of the two mechanisms of action is to be developed.

The widespread use of proton exchange membrane water electrolyzers (PEMWE) is hindered by their high cost, of which a colossal factor is caused by the bipolar plates (BPP). In this paper, we investigate the stability of two BPP materials on‐line with an optimized scanning flow cell setup coupled to an inductively coupled plasma mass spectrometer (SFC‐ICP‐MS), as well as scanning electron microscopy (SEM). The stability of currently used titanium and a cheaper alternative, stainless steel (SS) 316L, were characterized in deionized (DI) water and 0.5 mM H 2 SO 4 to mimic the conditions at the BPP under operation. We show that the dissolution of Ti is negligible, whereas SS 316L degrades notably. Here, besides pH, the applied potentials play a crucial role. Nonetheless, even for the highest measured dissolution rate of SS 316L, the contamination in a full cell is estimated to remain below 1 ppm. This work illustrates the capabilities of on‐line high‐throughput stability tests for BPP materials and could therefore contribute towards optimization of cost‐effective PEMWE.