A general framework derives exact outcome weights for double machine learning and generalized random forest estimators, showing that standard implementations are only scale-normalized rather than fully-normalized.
Simple Diagnostics for Two-Way Fixed Effects
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Difference-in-differences estimation is a widely used method of program evaluation. When treatment is implemented in different places at different times, researchers often use two-way fixed effects to control for location-specific and period-specific shocks. Such estimates can be severely biased when treatment effects change over time within treated units. I review the sources of this bias and propose several simple diagnostics for assessing its likely severity. I illustrate these tools through a case study of free primary education in Sub-Saharan Africa.
fields
econ.EM 1years
2024 1verdicts
ACCEPT 1representative citing papers
citing papers explorer
-
Treatment Effect Estimators as Weighted Outcomes
A general framework derives exact outcome weights for double machine learning and generalized random forest estimators, showing that standard implementations are only scale-normalized rather than fully-normalized.