Timo Schenk


PhD candidate @ University of Amsterdam

Contact me at t.d.schenk [at] uva.nl

Download my full CV here

Personal Website

Welcome! I am a PhD candidate at the University of Amsterdam and the Tinbergen Institute.

This summer I will join Aarhus University as a Postdoctoral Scholar. In January 2025 I will start as Assistant Professor (tenure track) at Erasmus University Rotterdam.

My research advances the econometric methods in the field of causal inference with panel data. My current papers have applications in environmental economics, economic history and public health.


Fields: Econometrics, Causal inference, Applied microeconometrics

Working papers

  1. Mediation Analysis in Difference-in-Differences Designs [Job Market Paper] (honorable mention at the IAAE 2023)
    Abstract This paper develops strategies to understand the mechanisms behind treatment effects in difference-in-differences (DiD) designs. Building on concepts from mediation analysis, I present identification strategies for the part of the average treatment effect that is caused by the treatment affecting a mediating variable. The sequential DiD approach requires additional parallel trend assumptions, a restriction on the mediator effect heterogeneity, and monotonicity of the treatment effect on the mediator. To avoid some of these restrictions, I present a two-sample approach, which includes results from other studies. I propose robust inference procedures on the proportion of the total effect a particular channel can explain. I revisit two empirical studies to show how researchers can use these approaches in practice.

  2. Time-Weighted Difference-in-Differences: Accounting for Common Factors in Short T Panels [WP2] (R&R Journal of Business & Economic Statistics)
    Abstract I propose a time-weighted difference-in-differences (TWDID) estimation approach that is robust against time-varying common factors in short T panels. Time weighting substantially reduces both bias and variance compared to an unweighted DID estimator through balancing the pre-treatment and post-treatment factors. To conduct valid inference on the average treatment effect, I develop a correction term that adjusts conventional standard errors for the presence of weight estimation uncertainty. Revisiting a study on the effect of a cap-and-trade program on NOx emissions, TWDID estimation reduces the standard errors of the estimated treatment effect by 10% compared to a conventional DID approach.

Work in progress

Scheduled Talks


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