Chen-Wei Hsiang

PhD Student @ UCL Econ

Education

University College London
  MPhil/PhD in Economics, 2022 -
University College London
  MRes in Economics, 2022
National Taiwan University
  M.A. in Economics, 2019
National Taipei University
  B.A. in Economics, 2017

Research Interest

  • Causal Machine Learning
  • High-Dimensional Causal Inference
  • Matrix Completion and Synthetic Control
  • Quantile and Distribution Methods

Publication, Working Paper & Work in Progress

Working Paper & Work in Progress

  • Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities
    arXiv working paper, 2025
    Ben Deaner, Chen-Wei Hsiang, Andrei Zeleneev
    Abstract: The presence of unobserved confounders is one of the main challenges in identifying treatment effects. In this paper, we propose a new approach to causal inference using panel data with large N and T. Our approach imputes the untreated potential outcomes for treated units using the outcomes for untreated individuals with similar values of the latent confounders. In order to find units with similar latent characteristics, we utilize long pre-treatment histories of the outcomes. Our analysis is based on a nonparametric, nonlinear, and nonseparable factor model for untreated potential outcomes and treatments. The model satisfies minimal smoothness requirements. We impute both missing counterfactual outcomes and propensity scores using kernel smoothing based on the constructed measure of latent similarity between units, and demonstrate that our estimates can achieve the optimal nonparametric rate of convergence up to log terms. Using these estimates, we construct a doubly robust estimator of the period-specifc average treatment effect on the treated (ATT), and provide conditions, under which this estimator is root-N-consistent, and asymptotically normal and unbiased. Our simulation study demonstrates that our method provides accurate inference for a wide range of data generating processes.
  • Event Study with Time-Adjusted Synthetic Control

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.

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
  • ECON1006: 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)

Award & Fellowship

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

Professional Service

  • Econometrics Brown Bag Seminar Co-organiser, University College London, 2023 -

Skill

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


(Last updated: May 2025)