Matching and Difference in Difference (DID) are two widespread methods that use pre-treatment outcomes to correct for … values. I find that Matching generally underestimates the average causal effect of the program and gets closer to the true … still performs better than Matching, especially in the middle of the life-cycle. These results are consistent with estimates …

-- Experiments -- Matching -- Instrumental Variables -- Regression Discontinuity Design -- Panel Data and fixed effects -- Difference … packages for specific causal inference techniques like ggdag, Matching, rdrobust, dosearch etc. are used in the book. The book …, matching, panel data, difference-in-differences, regression discontinuity design, instrumental variables and meta …

This paper compares matching and Difference-In-Difference matching (DID) when estimating the effect of a program on a … matching on past outcomes is not. Unfortunately, the consistency of both estimators relies on conditions that are at odds with … conditions severely bias both estimators. The behavior of matching is nevertheless less erratic: its bias generally decreases …

market on productivity and employment with firm-level data using matching and difference-in-difference techniques. We end up …

The authors present various matching estimators of the impact on earnings for individuals who attended public and …