Working Paper: CEPR ID: DP15092
Authors: Sokbae Lee; Bernard Salani
Abstract: Multivalued treatments are commonplace in applications. We explore the use of discrete-valued instruments to control for selection bias in this setting. We establish conditions under which counterfactual averages and treatment effects are identified for heterogeneous complier groups. These conditions require a combination of assumptions that restrict both the unobserved heterogeneity in treatment assignment and how the instruments target the treatments. We introduce the concept of filtered treatment, which takes into account limitations in the analyst’s information. Finally, we illustrate the usefulness of our framework by applying it to data from the Student Achievement and Retention Project and the Head Start Impact Study.
Keywords: identification; selection; multivalued treatments; discrete instruments; unordered monotonicity; factorial design
JEL Codes: No JEL codes provided
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| unordered monotonicity and strict targeting (C69) | identification of treatment effects for heterogeneous complier groups (C32) |
| effective targeting of instruments (E52) | maximization of relative mean utility of treatments (D81) |
| aggregation of treatment choices (D79) | complication in identification of causal effects (C32) |
| aggregation of different treatment effects (C22) | large intent-to-treat effect (C90) |
| filtering treatments (C22) | insights into treatment dynamics (C22) |