Working Paper: NBER ID: w24963
Authors: Susan Athey; Guido W. Imbens
Abstract: In this paper we study estimation of and inference for average treatment effects in a setting with panel data. We focus on the setting where units, e.g., individuals, firms, or states, adopt the policy or treatment of interest at a particular point in time, and then remain exposed to this treatment at all times afterwards. We take a design perspective where we investigate the properties of estimators and procedures given assumptions on the assignment process. We show that under random assignment of the adoption date the standard Difference-In-Differences estimator is an unbiased estimator of a particular weighted average causal effect. We characterize the properties of this estimand, and show that the standard variance estimator is conservative.
Keywords: No keywords provided
JEL Codes: C01; C23; C31
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| random adoption date (Y70) | unbiased DID estimator (C51) |
| random adoption date (Y70) | weighted average causal effect (C22) |
| DID estimator (C51) | captures effect of switching from never adopting to adopting in the first period (C24) |
| random adoption date (Y70) | conservative variance of DID estimator (C51) |