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倍差法STATA命令
MPRA
Simplifying the Estimation of Difference in Differences Treatment Effects with Stata*
Juan M. Villa
Brooks World Poverty Institute
University of Manchester
Manchester, UK.
juan.villalora@postgrad.manchester.ac.uk
*** DRAFT VERSION ***
Abstract. This paper explains the insights of the Statas user written command diff for
the estimation of Difference in Differences treatment effects (DID). The options and the
formulas are detailed for the single DID, Kernel Propensity Score DID, Quantile DID and the
balancing properties . An example of the features of diff is presented by using the dataset
from Card and Krueger (1994).
Keywords: Difference in differences, causal inference, kernel propensity score, quantile
treatment effects, quasi-experiments.
1. Introduction
Difference in Differences treatment effects (DID) have been widely used when the
evaluation of a given intervention entails the collection of panel data or repeated cross
sections. DID integrates the advances of the fixed effects estimators with the causal
inference analysis when unobserved events or characteristics confound the interpretations
(Angrist and Pischke, 2008).
Despite the existence of other plausible methods based on the availability of observational
data for quasi-experimental causal inference -i.e. matching methods, instrumental variable,
regression discontinuity-, DID estimations offer an alternative reaching the
unconfoundedness by controlling for unobserved characteristics and combining it with
observed or complementary information. Additionally, the DID is a flexible form of causal
inference because it c
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