Working Paper: NBER ID: w21313
Authors: Susan Athey; Dean Eckles; Guido W. Imbens
Abstract: We study the calculation of exact p-values for a large class of non-sharp null hypotheses about treatment effects in a setting with data from experiments involving members of a single connected network. The class includes null hypotheses that limit the effect of one unit's treatment status on another according to the distance between units; for example, the hypothesis might specify that the treatment status of immediate neighbors has no effect, or that units more than two edges away have no effect. We also consider hypotheses concerning the validity of sparsification of a network (for example based on the strength of ties) and hypotheses restricting heterogeneity in peer effects (so that, for example, only the number or fraction treated among neighboring units matters). Our general approach is to define an artificial experiment, such that the null hypothesis that was not sharp for the original experiment is sharp for the artificial experiment, and such that the randomization analysis for the artificial experiment is validated by the design of the original experiment.
Keywords: No keywords provided
JEL Codes: C01; C1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| treatment status of individuals within a network (D85) | outcomes of individuals (I26) |
| treatment status of immediate neighbors (R20) | outcomes of individuals (I26) |
| treatment status of individuals more than two edges away (C21) | outcomes of individuals (I26) |
| strength of ties within the network (D85) | treatment effects (C22) |