Estimation under unconfoundedness using matching and propensity score weighting

economics

Description

We have discussed the following causal inference methods in class 

• Randomized experiments 

• Estimation under unconfoundedness using matching and propensity score weighting 

• Instrumental variables 

• Difference-in-differences 

• Synthetic control 

• Regression discontinuity This assignment is about exploring how the estimators perform under different data generating processes (DGPs). Specifically, pick two or three estimators and do the following for each estimator: 

• Generate data using two DGPs 1. DGP1 - does not violate the assumptions under which the estimator works 2. DGP2 - violates at least one of the assumptions 

• For each DGP, describe it and explain how it does/does not satisfy the requirements for identification of the parameters (and which parameters are you identifying?) 

• Also, give a real life example of a situation which might be consistent with this DGP – Feel free (not required) to illustrate with a DAG 

• Run a Monte Carlo simulation. At each replication 1. Generate a random draw from the DGP

Instruction Files

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