Welcome to my webpage!
I am a postdoctoral fellow at Nuffield College and the University of Oxford. My research interests lie in Econometrics and especially in Causal Inference and Nonparametric Econometrics. I received my Ph.D. in Economics from the University of Mannheim.
You can download my CV here.
The confidence intervals (CIs) commonly reported in empirical fuzzy regression discontinuity studies are justified by theoretical arguments which assume that the running variable is continuously distributed with positive density around the cutoff, and that the jump in treatment probabilities at the cutoff is “large”. In this paper, we provide new confidence sets (CSs) that do not rely on such assumptions. Their construction is analogous to that of Anderson-Rubin CSs in the literature on instrumental variable models. Our CSs are based on local linear regression, and are bias-aware, in the sense that they explicitly take the possible smoothing bias into account. They are valid under a wide range of empirically relevant conditions in which existing CIs generally fail. These conditions include discrete running variables, donut designs, and weak identification. But our CS also perform favorably relative to existing CIs in the canonical setting with a continuous running variable, and can thus be used in all fuzzy regression discontinuity applications.
Analysis of LATE Estimates to a Violation of the Monotonicity
July 2021  [arxiv]
This paper presents a method to assess the sensitivity of treatment effect estimates to potential violations of the monotonicity assumption. I propose a model in which the degree to which monotonicity is violated is measured by two sensitivity parameters: One determines the population size of defiers and the other treatment effect heterogeneity between compliers and defiers. I identify the breakdown frontier, which is the set of sensitivity parameters that imply the weakest assumptions, which are necessary to draw a particular empirical conclusion, e.g. the average treatment effect is positive. Evaluating the plausibility of these parameters allows researchers to assess the credibility of this conclusion. I show how to conduct inference on these parameter estimates, where confidence sets are obtained through a bootstrap method. The performance of the breakdown frontier estimator is evaluated in a Monte Carlo study and illustrated in an empirical example.
Covariate Adjustments in Regression Discontinuity Designs
with Tomasz Olma and Christoph Rothe.
July 2021  [arxiv]
Empirical regression discontinuity (RD) studies often use covariates to increase the precision of their estimates. In this paper, we propose a novel class of estimators that use such covariate information more efficiently than the linear adjustment estimators that are currently used widely in practice. Our approach can accommodate a possibly large number of either discrete or continuous covariates. It involves running a standard RD analysis with an appropriately modified outcome variable, which takes the form of the difference between the original outcome and a function of the covariates. We characterize the function that leads to the estimator with the smallest asymptotic variance, and show how it can be estimated via modern machine learning, nonparametric regression, or classical parametric methods. The resulting estimator is easy to implement, as tuning parameters can be chosen as in a conventional RD analysis. An extensive simulation study illustrates the performance of our approach.