Assistant Professor Department of Quantitative Theory & Methods Goizueta Business School (by courtesy) Department of Economics (by courtesy) Emory University Address: 581 PAIS Building, 36 Eagle Row, Atlanta, 30322 ruoxuan.xiong@emory.edu [Google Scholar] [CV]

I am an assistant professor at Emory University since Fall 2021. My research lies at the intersection of causal inference, machine learning, experimental design, and statistical inference. My work is driven by emerging problems and challenges in digital platforms, finance, and healthcare. My two main research directions are:

• Experimental design, causal machine learning, causal foundation models, and fine-tuning;

• Machine learning for financial big data.

I received my Ph.D. in Management Science and Engineering from Stanford and my B.S. from Peking University. I was a postdoctoral researcher at the Stanford Graduate School of Business. More information can be found in my CV.

Research

Working Papers

• Data-Driven Switchback Experiments: Theoretical Tradeoffs and Empirical Bayes Designs [slides]
  R. Xiong, A. Chin. and S. J. Taylor
  

• Factor Analysis for Causal Inference on Large Non-Stationary Panels with Endogenous Treatment [slides]
  J. Duan*, M. Pelger* and R. Xiong*
  

• Semiparametric Estimation of Treatment Effects in Observational Studies with Heterogeneous Partial Interference [slides] [talk] [code]
  Z. Qu*, R. Xiong*, J. Liu, and G. Imbens
  

Journal Publications

• Optimal Experimental Design for Staggered Rollouts [slides] [code]
  R. Xiong, S. Athey, M. Bayati, and G. Imbens
  Management Science, 2024, 70(8), 5317-5336
  2020 MSOM Student Paper Competition Finalist

• Target PCA: Transfer Learning Large Dimensional Panel Data [slides]
  Internet Appendix
  J. Duan*, M. Pelger*, and R. Xiong*
  Journal of Econometrics, 2023
  

• Federated Causal Inference in Heterogeneous Observational Data [slides] [code] [lay abstract]
  R. Xiong, A. Koenecke, M. Powell, Z. Shen, J. T. Vogelstein, and S. Athey
  Statistics in Medicine, 2023, 1–22, doi: 10.1002/sim.9868
  

• Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference [slides]
  Internet Appendix
  R. Xiong and M. Pelger
  Journal of Econometrics, 2023, 233(1), 271-301
  2019 George Nicholson Student Paper Competition Honorable Mention

• Interpretable Sparse Proximate Factors for Large Dimensions [slides]
  M. Pelger* and R.Xiong*
  Journal of Business & Economic Statistics, 2022, 40(4), 1642-1664
  

• State-Varying Factor Models of Large Dimensions [slides]
  Internet Appendix
  M. Pelger* and R.Xiong*
  Journal of Business & Economic Statistics, 2022, 40(3), 1315-1333
  

Conference Publications

• Higher-Order Causal Message Passing for Experimentation Under Unknown Interference
  M. Bayati*, Y. Luo*, W. Overman*, S. Shirani*, and R. Xiong*
  NeurIPS 2024
  

• Two-Stage Shadow Inclusion Estimation: An IV Approach for Causal Inference under Latent Confounding and Collider Bias
  B. Li, A. Wu, R. Xiong, and K. Kuang
  ICML 2024
  

• Learning Shadow Variable Representation for Treatment Effect Estimation under Collider Bias
  B. Li, H. Li, R. Xiong, A. Wu, F. Wu, and K. Kuang
  ICML 2024
  

• Stable Estimation of Heterogeneous Treatment Effects
  A. Wu, K. Kuang, R. Xiong, B. Li, and F. Wu
  ICML 2023
  

• Stable Prediction with Model Misspecification and Agnostic Distribution Shift
  K. Kuang, R. Xiong, P. Cui, S. Athey, and B. Li
  AAAI 2020
  

• Stable Predictions across Unknown Environments
  K. Kuang, P. Cui, S. Athey, R. Xiong, and B. Li
  KDD 2018 (oral)
  

Idle Manuscripts

• Deep Learning Stock Volatility with Google Domestic Trends
  R. Xiong, E. Nichols, and Y. Shen
  

Teaching

Here are the courses I have been teaching:

• QTM 347, Machine Learning: Fall 2023, Spring 2024, Fall 2024, Spring 2025

• QTM 385, Machine Learning and Causal Inference: Fall 2021, Spring 2022, Fall 2022

• QTM 385, Quantitative Finance: Fall 2022, Spring 2023, Fall 2023, Fall 2024

Education

• 2021: Postdoc, Graduate School of Business, Stanford University

• 2020: Ph.D., Department of Management Science and Engineering, Stanford University

• 2014: B.S., Peking University