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...

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 show that an insight from taxation theory allows identification of both the supply and demand elasticities with only one instrument. Ramsey (1928) and subsequent models of taxation assume that a tax levied on the demand side only affects demand through the price after taxation....

We show that an insight from taxation theory allows identification of both the supply and demand elasticities with only one instrument. Ramsey (1928) and subsequent models of taxation assume that a tax levied on the demand side only affects demand through the price after taxation....

We show that an insight from taxation theory allows identification of both the supply and demand elasticities with only one instrument. Ramsey (1928) and subsequent models of taxation assume that a tax levied on the demand side only affects demand through the price after taxation....

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...