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A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions
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Causal message-passing for experiments with unknown and general network interference.

Sadegh Shirani1, Mohsen Bayati1

  • 1Operations, Information & Technology, Graduate School of Business, Stanford University, Stanford, CA 94305.

Proceedings of the National Academy of Sciences of the United States of America
|September 27, 2024
PubMed
Summary
This summary is machine-generated.

Network interference can bias randomized experiments. This study introduces causal message-passing to accurately estimate treatment effects in complex networks, even before effects stabilize. This method improves data-driven decision-making.

Keywords:
approximate message-passingexperiment designnetwork interferencetotal treatment effect

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Area of Science:

  • Statistics
  • Causal Inference
  • Network Analysis

Background:

  • Randomized experiments are crucial for evaluating interventions but can be invalidated by network interference.
  • Network interference occurs when a unit's treatment affects connected units, biasing traditional estimations.
  • Existing models struggle with complex and unknown network interference patterns.

Purpose of the Study:

  • To introduce a novel framework, causal message-passing, to address complex and unknown network interference in randomized experiments.
  • To develop a method suitable for multiperiod experiments with numerous units and significant interference.
  • To enable accurate estimation of treatment effects in the presence of network spillover effects.

Main Methods:

  • The study grounds its framework in high-dimensional approximate message-passing methodology.
  • Causal effects are modeled as a dynamic process of impact propagation through the network.
  • A practical algorithm is developed to estimate the total treatment effect, comparing all-treated to no-treated scenarios.

Main Results:

  • The causal message-passing framework effectively accommodates complex and unknown network interference.
  • The approach allows approximation of potential outcomes dynamics over time, extracting information before equilibrium.
  • Effectiveness demonstrated across five numerical scenarios with diverse interference structures.

Conclusions:

  • Causal message-passing provides a robust method for analyzing randomized experiments with network interference.
  • This framework advances the field by moving beyond specialized models for interference.
  • The developed algorithm offers a practical tool for estimating total treatment effects in networked settings.