Research

Working Paper & Work in Progress

• Simple Specification-Free Causal Inference in Large Panels
  arXiv working paper, 2026; [web link]
  Ben Deaner, Chen-Wei Hsiang, Andrei Zeleneev
  Previously circulated as Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities (2025)
  
  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
  
  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; [web link]
  Jau-er Chen, Chen-Wei Hsiang
  
  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.

Code

• PanelIFE [GitHub link]
  R package for "Robust Estimation and Inference in Panels with Interactive Fixed Effects (Armstrong, Weidner, and Zeleneev, 2026) [web link]"
  
  PanelIFE implements the estimation and inference procedure for panel data with interactive fixed effects. This package provides two different method for estimation: one is the commonly used linear panel data model estimation procedure, and another one is the bias-aware estimation procedure that allows weak factors.

• PanelLatSim [GitHub link]
  R package for "Simple Specification-Free Causal Inference in Large Panels (Deaner, Hsiang, and Zeleneev, 2026) [web link]"
  
  PanelLatSim implements the doubly-robust estimation and inference procedure of Deaner, Hsiang, and Zeleneev (2026) for the period-specific average treatment effect on the treated (ATT) in large panels with unobserved confounders. The untreated potential outcomes and the treatments are allowed to follow a nonparametric, nonlinear, and non-separable factor model, which is substantially more general than the additive structure imposed by two-way fixed effects and the interactive structure imposed by synthetic control and matrix completion methods. The missing counterfactual outcomes and propensity scores are imputed by kernel smoothing over a pseudo-distance that uncovers latent similarities between units from their long pre-treatment histories, and the imputed nuisances are combined into a doubly-robust estimator with cross-fitting.

_{^{(Last updated: Oct 2026)}}