Abstract Difference-in-difference (DD) methods are a common strategy for evaluating the
effects of policies or programs that are instituted at a particular point in time, such as the …

This paper explains the insights of the Stata's user written command diff for the estimation of
Difference in Differences treatment effects (DID). The options and the formulas are detailed …

In this article, I present the features of the user-written command diff, which estimates
difference-in-differences (DID) treatment effects. diff simplifies the DID analysis by allowing …

This survey gives a brief overview of the literature on the difference-in-difference (DiD)
estimation strategy and discusses major issues using a treatment effects perspective. In this …

Objective To define confounding bias in difference‐in‐difference studies and compare
regression‐and matching‐based estimators designed to correct bias due to observed …

Difference-in-differences (DID) analysis is used widely to estimate the causal effects of
health policies and interventions. A critical assumption in DID is “parallel trends”: that pre …

Objective To evaluate the effects of specification choices on the accuracy of estimates in
difference‐in‐differences (DID) models. Data Sources Process‐of‐care quality data from …

Abstract Difference-in-differences (DiD) estimators provide unbiased treatment effect
estimates when, in the absence of treatment, the average outcomes for the treated and …

Difference-in-differences is one of the most used identification strategies in empirical work in
economics. This chapter reviews a number of important, recent developments related to …

Difference-in-difference (DID) estimators are a valuable method for identifying causal effects
in the public health researcher's toolkit. A growing methods literature points out potential …