讲座题目：Causal Inference with Time-Series Cross-Sectional Data
主持人：谢岳，澳门太阳城 - 澳门太阳城平台长聘教授
Difference-in-differences and two-way fixed effects models are commonly used for causal inference with time-series cross-sectional (TSCS) data. They require the “parallel trends” assumption, which states that the average outcomes of treated and control units would have followed parallel paths in the absence of the treatment. In practice, this assumption is often violated due to the presence of time-varying confounders. To address this problem, I introduce two novel methods: the generalized synthetic control method (Xu 2017) and trajectory balancing (Hazlett and Xu 2018). The former adopts a model-based approach and imputes treated counterfactuals using a latent factor model; the latter employs a reweighting approach and seeks balance in pre-treatment outcome trajectories and covariates between the treatment and control groups. I illustrate these two methods using several empirical examples from political science.