Working Paper: NBER ID: w20535
Authors: Nathaniel Baum-Snow; Fernando Ferreira
Abstract: Recovery of causal relationships in data is an essential part of scholarly inquiry in the social sciences. This chapter discusses strategies that have been successfully used in urban and regional economics for recovering such causal relationships. Essential to any successful empirical inquiry is careful consideration of the sources of variation in the data that identify parameters of interest. Interpretation of such parameters should take into account the potential for their heterogeneity as a function of both observables and unobservables.
Keywords: No keywords provided
JEL Codes: R0
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| sources of variation in data (C81) | credible causal inference (C32) |
| treatment effect heterogeneity (C21) | varying causal relationships (C32) |
| omitted variables (C29) | biased estimates (C51) |
| self-selection and non-compliance (C83) | complicate interpretation of treatment effects (C32) |
| differences in differences (J79) | improved credibility of treatment effect estimates (C21) |
| randomized trials (C90) | recovering treatment effects (C22) |
| randomization (C90) | alleviate concerns about endogeneity (C51) |