Chen-Wei Hsiang

PhD Candidate @ UCL

Education

University College London
PhD in Economics, 2027 (expected)
  Advisors: Andrei Zeleneev & Ben Deaner
University College London
MRes in Economics, 2022
  Dissertation title: Event Study with Time-Adjusted Synthetic Control
  Advisors: Andrei Zeleneev
National Taiwan University
M.A. in Economics, 2019
  Dissertation title: Causal Random Forests Model Using Instrumental Variable Quantile Regression
  Advisors: Jau-er Chen
National Taipei University
B.A. in Economics, 2017

Research Interest

  • Nonparametric panel data
  • Network model
  • Synthetic control and matrix completion
  • Optimal transport
  • Causal machine learning and high-dimensional causal inference
  • Quantile and distribution methods

Publication, Working Paper & Work in Progress

Working Paper & Work in Progress

  • Simple Specification-Free Causal Inference in Large Panels
    arXiv working paper, 2025
    Ben Deaner, Chen-Wei Hsiang, Andrei Zeleneev
    Previously circulated as Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities (2025)
    Abstract: We propose a simple and transparent approach to causal inference using panel data with a large number of units and periods. Instead of imposing a particular parametric specification on the untreated potential outcomes, we assume they follow a nonparametric nonseparable factor model. We use long pre-treatment outcome histories to construct a measure of similarity between the latent factors of different units. Using this measure, we impute missing counterfactual means and propensity scores through kernel smoothing. Under weak smoothness conditions, these estimates can attain the optimal nonparametric convergence rate up to logarithmic factors, provided that the pre-treatment history grows sufficiently quickly with the sample size. We construct a doubly robust estimator of the period-specific average treatment effect on the treated (ATT) and provide conditions under which it is root-N-consistent and asymptotically normal, centered at the true ATT. Simulations demonstrate accurate inference for a wide range of linear and nonlinear data-generating processes.
  • Event Study with Time-Adjusted Synthetic Control
    Abstract: This research proposes a time-adjusted synthetic control method for the event study. The proposed method utilizes a two-step approach in constructing time weights based on the factor model with interactive fixed effects and unit weights modified from the conventional synthetic control method. In the simulation study, under the data generating process with heteroscedasticity across time, the proposed method has the advantage of efficiency with large panel data.

Publication

  • Causal Random Forests Model Using Instrumental Variable Quantile Regression
    Econometrics, 2019, 7(4), 1-22.
    Jau-er Chen, Chen-Wei Hsiang
    Abstract: We propose an econometric procedure based mainly on the generalized random forests method. Not only does this process estimate the quantile treatment effect nonparametrically, but our procedure yields a measure of variable importance in terms of heterogeneity among control variables. We also apply the proposed procedure to reinvestigate the distributional effect of 401(k) participation on net financial assets, and the quantile earnings effect of participating in a job training program.

Conferences & Seminar Presentations

UCL Econometrics Brown Bag Seminar, 2023, 2024, 2025, 2026
UCL-CeMMAP Ph.D./PostDoc Econometrics Research Day, 2023, 2024, 2026
IMS International Conference on Statistics and Data Science (Contributed Session), 2025
Econometric Society World Congress, 2025

Award & Fellowship

PhD Scholar, Centre for Microdata Methods and Practice (CeMMAP), 2023 - present
Best Newcomer Teaching Assistant, Department of Economics, University College London, 2023
Asia-Pacific Economic Cooperation (APEC) - Healthy Women, Healthy Economies Research Prize, 2021

Referee Service

Journal of Econometrics, Economic Modelling, Econometrics

Professional Service

UCL Econometrics Brown Bag Seminar Co-organiser, 2023 - 2026

Research Experience

Department of Economics, University College London
Research Assistant (to Dr. Andrei Zeleneev and Prof. Martin Weidner), 2024
  • Project on Weak Factors: Writing R package and corresponding vignette for method in panels with factor structure
Behavioral and Data Science Research Center, National Taiwan University
Research Assistant (to Prof. Ming-Jen Lin and Prof. Shiau-Fang Chao), 2019 - 2021
  • Project: Application of Government Big Data in Computational Social Welfare: Example from Long-Term Care
Department of Economics, National Taiwan University
Research Assistant (to Prof. Yi-Chan Tsai), 2018 - 2019
  • Project: Consumption Inequality in Taiwan since 1986
  • Project: Using Electronic Invoice Data to Analyze Private Consumption

Teaching Experience

Department of Economics, University College London
Postgraduate Teaching Assistant, 2022 -
  • ECON0064: MSc Econometrics
  • ECON0005: BSc Statistical Methods in Economics
Department of Economics, National Taiwan University
Teaching Assistant, 2017 - 2019
  • ECON7203: MA/PhD Applied Microeconomics (I)
  • ECON4035: BA International Economics Principle
  • ECON1004: BA Principle of Economics (I)
  • ECON1006: BA Economics (I)

Skill

Programming Languages: R, Python, Julia, Matlab, SQL, C/C++


(Last updated: Oct 2026)