Design and analysis of experiments in networks: Reducing bias from interference (1404.7530v2)

Abstract: 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.

• The paper demonstrates that graph cluster randomization effectively reduces bias from interference in causal network experiments.
• Simulations reveal up to a 40% reduction in RMSE, especially in networks with high clustering and strong peer effects.
• The study enhances causal inference through innovative design methods, offering practical insights for social media and epidemiology applications.

Design and Analysis of Experiments in Networks: Reducing Bias from Interference

The paper by Dean Eckles, Brian Karrer, and Johan Ugander addresses a significant challenge in causal inference: the interference phenomenon in networked environments. In network settings, the outcome for a unit often depends not only on its treatment but also on the treatment and behavior of other units, introducing biases that invalidate traditional analysis methods. This research evaluates experimental design and analysis methodologies aimed at minimizing such biases.

The researchers focus on realistic assumptions, acknowledging that entirely eliminating interference in connected networks is implausible. Instead, the paper assesses methods that seek to mitigate this bias through structured experimental designs and analytical adjustments. In particular, it examines two primary approaches: graph cluster randomization, which involves clustering correlated units for treatment assignments, and incorporating network neighbors' treatment information during analysis.

The paper establishes sufficient conditions for reducing bias using theoretical models and simulations. The bias reduction is most pronounced in networks with high clustering and strong treatment and interaction effects. Significant findings reveal that graph cluster randomization can dramatically decrease bias without excessively increasing variance, particularly in networks with pronounced local clustering and robust social interactions.

From a numerical standpoint, the simulations showcase substantial bias and error reduction with graph cluster randomization, particularly when peer effects are pronounced relative to the baseline. For instance, the results demonstrate a 40% reduction in RMSE with clustered assignments in small-world networks, contingent on high peer interaction and clustering coefficients.

The implications of these methods are both practical and theoretical. Practically, these methodologies provide more accurate estimates of treatment effects in networked environments, which can be applied in various fields such as social media and epidemiology. Theoretically, the paper contributes a rigorous formalization of experimentation in networks, enhancing the understanding of causal inference in these complex structures.

Future developments in this domain may explore extending these results to broader classes of networks and more intricate data-generating processes. Furthermore, incorporating machine learning methodologies could yield more refined techniques for identifying and reducing bias in networked data. Overall, this research equips practitioners with robust tools for conducting causal experiments in networked systems, advancing both the methodology and understanding of interference in such contexts.