بحث بعنوان Design and Analysis of Experiments in Networks – Reducing Bias From Interference

بحث بعنوان
Design and Analysis of Experiments in Networks – Reducing Bias From Interference
By Dean Eckles Brian Karrer and Johan Ugander
Facebook, Inc., Facebook, Inc., and Cornell University
Estimating the effects of interventions in networks is complicated when
the units are interacting, such that the outcomes for one unit may depend
on the treatment assignment and behavior of many or all other units (i.e.,
there is interference). When most or all units are in a single connected component, it is impossible to directly experimentally compare outcomes under
two or more global treatment assignments since the network can only be observed under a single assignment. Familiar formalism, experimental designs,
and analysis methods assume the absence of these interactions, and result in
biased estimators of causal effects of interest. While some assumptions can
lead to unbiased estimators, these assumptions are generally unrealistic, and
we focus this work on realistic assumptions. Thus, in this work, we evaluate methods for designing and analyzing randomized experiments that aim to
reduce this bias and thereby reduce overall error. In design, we consider the
ability to perform random assignment to treatments that is correlated in the
network, such as through graph cluster randomization. In analysis, we consider incorporating information about the treatment assignment of network
neighbors. We prove sufficient conditions for bias reduction through both design and analysis in the presence of potentially global interference. Through
simulations of the entire process of experimentation in networks, we measure the performance of these methods under varied network structure and
varied social behaviors, finding substantial bias and error reductions. These
improvements are largest for networks with more clustering and data generating processes with both stronger direct effects of the treatment and stronger
interactions between units.

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