Econ 570: Assignment 1
Due: 19 February 2021 at 2 pm Pacific Time
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 gen- erating 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
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2. Estimate the model 3. Save the estimates
• Report summary statistics of parameter estimates
1. Bias 2. RMSE 3. Size
• Comment on the results. Are the estimates from DGP1 and DGP2 as expected?
Turn in your code. The commentary can be in the form of a markup document or a separate pdf.
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