Working Paper: NBER ID: w18056
Authors: Anirban Basu
Abstract: This paper builds on the methods of local instrumental variables developed by Heckman and Vytlacil (1999, 2001, 2005) to estimate person-centered treatment (PeT) effects that are conditioned on the person's observed characteristics and averaged over the potential conditional distribution of unobserved characteristics that lead them to their observed treatment choices. PeT effects are more individualized than conditional treatment effects from a randomized setting with the same observed characteristics. PeT effects can be easily aggregated to construct any of the mean treatment effect parameters and, more importantly, are well-suited to comprehend individual-level treatment effect heterogeneity. The paper presents the theory behind PeT effects, studies their finite-sample properties using simulations and presents a novel analysis of treatment evaluation in health care.
Keywords: Person-Centered Treatment Effects; Instrumental Variables; Health Economics; Heterogeneity; Treatment Effect Estimation
JEL Codes: C21; C26; D04; I12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| PET effects (H43) | more nuanced alternative to CATE (C24) |
| PET effects (H43) | individualized understanding of treatment effects (C22) |
| unobserved confounders (C20) | treatment selection (C52) |
| local instrumental variables (C26) | capture distribution of unobserved characteristics (D39) |
| PET effects (H43) | mean treatment effect parameters (C22) |
| PET effects (H43) | individual-level treatment effect heterogeneity (C21) |
| observed characteristics + unobserved characteristics (C29) | treatment choices (D87) |