I am an Assistant Professor of Econometrics at the University of St. Gallen.
I hold a Ph.D. in Economics from the University of Cologne.
My research interest lies in the intersection of causal inference and machine learning with applications in health economics.
You can find my CV here.
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Abstract: Maternal smoking during pregnancy is a substantial threat to infants' health at birth, but beyond averages, its effects are not well understood. This study aims at identifying driving factors of heterogeneity in the effects of smoking on birth weight and other measures for health at birth. We propose a novel approach to decompose differences in treatment effects into factors driving heterogeneity, leveraging recent developments at the intersection of machine learning and econometrics. In a comprehensive sample of infants born in the US, we find that advanced mother's age is a risk factor amplifying the effect of smoking on health at birth. Weight-related features, such as high prepregnancy body mass index and excessive weight gain, prove useful in mitigating the negative effects of smoking on birth weight but have mixed effects on other health measures. These findings can be useful for improved allocation of intensified assistance with smoking cessation for pregnant women. The analysis implies that allocation based on the identified key factors of heterogeneity has the potential to generate large healthcare expenditure savings and improve infants' health at birth.
Abstract: Understanding in-utero exposure to extreme weather events is key to mitigating climate change’s impact on health at birth. Using detailed historic weather records and data on infants born in the US between 1989-2004, we investigate how in-utero exposure to weather events, such as heat and cold waves or rainfall, impacts infant’s health at birth. We focus on the effects of heat shocks on birth outcomes and systematically investigate heterogeneity therein using the causal forest, a recently developed causal machine learning technique. Exposure to a heat shock significantly reduces birth weight by around 6 grams on average and increases the small for gestational age (SGA) birth rate. We find substantial heterogeneity in the effect of heat shock exposure on birth weight. Especially infants born to black, Mexican, or low-educated mothers are disproportionately prone to health risks from extreme heat exposure.
Abstract: This paper introduces the Quantile Average Treatment Effect (QATE), a new parameter describing fine-grained causal heterogeneity. The QATE focuses on the 'actionable' part of the causal heterogeneity, which depends on observable characteristics of individual units only. We propose two estimators: a first-stage agnostic estimator, which can be applied with any causal machine learning method, and one specifically tailored to the Modified Causal Forest. A simulation analysis indicates that both estimators are consistent and informative about treatment effect heterogeneity. We illustrate their value in an empirical analysis of heterogeneity in the effect of smoking during pregnancy on birth weight.
Abstract: In this study we investigate the effects of introduction of smoking bans on smoking behavior among pregnant women, by exploiting regional differences in smoking ban introduction over time and across states. We estimate the effect of smoking bans on average cigarette consumption and smoking rate among pregnant women using a difference-in-differences approach. In a comprehensive dataset containing information on nearly all births in Germany, we find that the introduction of smoking bans has a significant decreasing effect on average number of cigarettes smoked by pregnant women, whereas there is no effect on smoking rate. Considering regional differences in smoking ban implementation, we find that especially strict smoking bans have strong effects on decreasing average number of cigarettes smoked, whereas partial smoking bans are less effective.