Working Paper: NBER ID: w26104
Authors: Guido Imbens
Abstract: In this essay I discuss potential outcome and graphical approaches to causality, and their relevance for empirical work in economics. I review some of the work on directed acyclic graphs, including the recent “The Book of Why,” ([Pearl and Mackenzie, 2018]). I also discuss the potential outcome framework developed by Rubin and coauthors, building on work by Neyman. I then discuss the relative merits of these approaches for empirical work in economics, focusing on the questions each answer well, and why much of the work in economics is closer in spirit to the potential outcome framework.
Keywords: No keywords provided
JEL Codes: C01
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Potential Outcome (PO) framework (D78) | traditional economic modeling (E19) |
| Potential Outcome (PO) framework (D78) | treatment effect heterogeneity (C21) |
| Potential Outcome (PO) framework (D78) | policy relevance (J68) |
| DAG approach (C69) | illustrating causal assumptions (C29) |
| DAG approach (C69) | clarifying complex relationships (L14) |
| DAG approach (C69) | answering causal queries (C32) |
| DAG approach (C69) | utility in complex models (C51) |
| Lack of empirical evidence for DAGs (E13) | lack of adoption in economics (D59) |
| Established identification strategies (F55) | preference over DAGs (C69) |