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
Econ570Assignment1.pdf
44.5 KB
