Working Paper: NBER ID: w31942
Authors: Dmitry Arkhangelsky; Guido Imbens
Abstract: In this survey we discuss the recent causal panel data literature. This recent literature has focused on credibly estimating causal effects of binary interventions in settings with longitudinal data, emphasizing practical advice for empirical researchers. It pays particular attention to heterogeneity in the causal effects, often in situations where few units are treated and with particular structures on the assignment pattern. The literature has extended earlier work on difference-in-differences or two-way-fixed-effect estimators. It has more generally incorporated factor models or interactive fixed effects. It has also developed novel methods using synthetic control approaches.
Keywords: causal inference; panel data; binary interventions; heterogeneity; synthetic control
JEL Codes: C23
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| binary interventions (C25) | causal effects (C22) |
| treatment effect heterogeneity (C21) | implications for causal inference (C20) |
| novel methods (synthetic control) (C90) | nuanced understanding of causal relationships (D80) |
| two-way fixed effects (TWFE) (C23) | biased results (J15) |
| two-way fixed effects (TWFE) (C23) | robust estimates (C51) |
| traditional methods (DiD) (C90) | potential bias (D91) |