This paper develops a machine-learning method that allows researchers to estimate heterogeneous treatment effects with panel data in a setting with many covariates. Our method, which we name the dynamic causal forest (DCF) method, extends the causal-forest method of Wager and Athey (2018) by...

We provide evidence on narratives about the macroeconomy-the stories people tell to explain macroeconomic phenomena-in the context of a historic surge in inflation. We measure economic narratives in open-ended survey responses and represent them as Directed Acyclic Graphs. We apply this approach...

This paper provides estimation and inference methods for conditional average treatment effects (CATE) characterized by a high‐dimensional parameter in both homogeneous cross‐sectional and unit‐heterogeneous dynamic panel data settings. In our leading example, we model CATE by interacting...