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. I will be available on the 2023/24 academic job market.

My research advances the econometric methods in the field of causal inference with panel data. In my job market paper, I develop strategies to identify the mechanisms behind treatment effects in difference-in-differences designs. My current papers have applications in environmental economics, economic history and public health.

My advisors are Frank Kleibergen and Andreas Pick. Last spring I was a Visiting Research Fellow at Brown University, invited by Toru Kitagawa. Before my graduate studies I was a Junior Research Manager at the Center for Evaluation and Development and I obtained my bachelor’s degree at the University of Mannheim.


Prof. Frank Kleibergen
Professor of Econometrics
University of Amsterdam
Dr. Andreas Pick
Associate Professor
Erasmus University Rotterdam

Prof. Toru Kitagawa
Professor of Economics
Brown University

Placement director: Prof. Eric Bartelsman (e.j.bartelsman[at]vu.nl)

Placement assistant: Christina Månsson (c.mansson[at]tinbergen.nl)


Field (primary): Econometrics

Field (secondary): 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


Undergraduate U Amsterdam: Econometricst,c, Mathematical and Empirical Financet, Empirical Projectt,c, Thesis supervision
U Mannheim: Microeconomics At,r, Statistics 1t,c, Mathematics for Economistst,r
Graduate U Amsterdam: Advanced Econometrics 1c, Financial Econometricsc
Tinbergen Institute: Measure Theory and Asymptotic Statisticst, Microeconomics 3: Information and Contract Theoryt
Other Causal inference (TA for an online class by Scott Cunningham); Introduction to STATA (Crash course for C4ED employees)

t theory tutorial (around 30 students), c computer lab (30), r revision lecture (80)


Current year (2023)

Paper presented: JMP

Previous year (2022)

Paper presented: WP2