Research
My research interests lie at the intersection of Causal Inference, Econometrics, and Machine Learning. Below are some of my current research projects.
1. Sequential Treatment Allocation with Interference
We study the sequential treatment allocation problem (a.k.a. multi-armed bandit problem) with interference. The work is motivated from the recent work of Jia et al. (2024). We propose some new algorithms for this problem and investigate their performance in the bandit setting.
Keywords: Bandits, Interference, Adaptive Experiments
2. Bias Correction, Bootstrap and Kernel Estimators
We propose a weighted bootstrap based confidence interval for the bias corrected local linear estimator. The idea of bias correction for the kernel estimators is motivated from the work of Calonico, Cattaneo, & Farrell (2018) and Cheng & Chen (2019). Here Cheng & Chen (2019) already proposed bias corrected paired bootstrap and proved theoretical results for uniform confidence intervals. In our work we propose a weighted bootstrap, and investigate the point-wise performance.
Keywords: Nonparametric Estimation, Bootstrap, Bias Correction, Kernel Methods
3. Autoregressive Dynamic Network Modeling with Serial and Cross Sectional Dependence
This is a joint work with my supervisor Professor Carsten Jentsch and an extension of my master thesis.
Keywords: Network Models, Time Series, Dependence Structures
Draft versions of the papers will be available soon.