Isea Cieply

PhD Student in Applied Statistics and Empirical Methods

Research
My PhD research investigates the heterogeneity of treatment effects across diverse subpopulations.
I apply both parametric (multiple linear regression) and non-parametric methods (causal machine learning, causal forests).

I sincerely appreciate the funding provided by the University of Göttingen's Equality Innovation Fund from September 2021 to August 2024 for our project 'The gender pay gap in the W-payroll system'.

My research explores how gender and gender roles affect labor market outcomes and how environmental governance shapes firms and their innovative behavior. So far, I have focused on European and East Asian contexts.

Research Interests

• Heterogeneity of Treatment Effects
• Nonparametric, non-linear methods: Causal Machine Learning, Causal Forests
• Parametric, multiple linear regression: Moderated Regression Analysis, Difference-in-Differences, and Fixed Effects Panel Data
• Applications in Environmental Economics and Gender Economics in Germany, China, and Japan

Publications

• Cieply, I., Wang, F. (2025). Does Pollution Control Foster Innovation? Quasi-Experimental Evidence From China's Two-Control Zone Policy, Review of Development Economics, https://doi.org/10.1111/rode.13182
• Cieply, I., Barros, L., Kis-Katos, K., Kneib, T., Silbersdorff, Werkmeister, A., Georgi, A. and Hayn, D. (2024). The Gender Pay Gap in the W-Payroll at the University of Göttingen: Executive Summary. https://www.uni-goettingen.de/en/the+gender+pay+gap+in+the+w-payroll+system/689666.html
• Peltokorpi, V., Cieply, I., Froese, F., The moderating effects of gender role orientations between the relationships of work-family conflict and turnover and being valued. International Journal of Psychology, 2023. https://doi.org/10.1002/ijop.13095
• Cieply, Isea, "Annual Meeting of the German Association for Chinese Studies University of Münster" (conference report), ASIEN 134, January 2015, http://asien.asienforschung.de/asien-134-januar-2015

Teaching

• Statistics: Exercise & Coaching (2022-2025)
• Statistics Tutor Seminar (2022-2025)
• Empirical Methods in Human Resource Management (2021)
• Current Topics in Human Resource Management (2021/2022)