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Exact p-Values for Network Interference
by
Eckles, Dean
, Athey, Susan
, Imbens, Guido W.
in
Artificial
/ Experiments
/ Fisher exact p-values
/ Hypotheses
/ Interactions
/ model validation
/ Networks
/ Null hypothesis
/ Randomization inference
/ Regression analysis
/ Spillovers
/ statistical analysis
/ Statistical methods
/ statistical models
/ Statistics
/ SUTVA
/ Theory and Methods
2018
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Exact p-Values for Network Interference
by
Eckles, Dean
, Athey, Susan
, Imbens, Guido W.
in
Artificial
/ Experiments
/ Fisher exact p-values
/ Hypotheses
/ Interactions
/ model validation
/ Networks
/ Null hypothesis
/ Randomization inference
/ Regression analysis
/ Spillovers
/ statistical analysis
/ Statistical methods
/ statistical models
/ Statistics
/ SUTVA
/ Theory and Methods
2018
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Exact p-Values for Network Interference
by
Eckles, Dean
, Athey, Susan
, Imbens, Guido W.
in
Artificial
/ Experiments
/ Fisher exact p-values
/ Hypotheses
/ Interactions
/ model validation
/ Networks
/ Null hypothesis
/ Randomization inference
/ Regression analysis
/ Spillovers
/ statistical analysis
/ Statistical methods
/ statistical models
/ Statistics
/ SUTVA
/ Theory and Methods
2018
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Journal Article
Exact p-Values for Network Interference
2018
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Overview
We study the calculation of exact p-values for a large class of nonsharp 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 (e.g., based on the strength of ties) and hypotheses restricting heterogeneity in peer effects (so that, e.g., 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.
Publisher
Taylor & Francis,Taylor & Francis Group,LLC,Taylor & Francis Ltd
Subject
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