Causal Inference Under Interference And Network Uncertainty.

Area of Science:

• Causal Inference
• Statistical Modeling
• Network Analysis

Background:

• Traditional statistical and causal inference methods often assume data independence, which is frequently violated in real-world scenarios.
• Existing methods for causal effects with data dependence require precise knowledge of the network structure, which is often unavailable.
• Uncertainty in network structure is common in fields like public health and social sciences, particularly in vulnerable communities.

Purpose of the Study:

• To develop a general method for estimating causal effects in the presence of data dependence when the network structure is not known a priori.
• To address the limitations of existing causal inference techniques that require a known dependence structure.
• To provide a robust framework for causal inference in complex, interconnected systems.

Main Methods:

• Combines techniques from structure learning and causal inference with interference.
• Develops a general method for estimating causal effects under unknown network dependence structures.
• Utilizes synthetic datasets exhibiting network dependence for validation.

Main Results:

• The proposed method effectively estimates causal effects even when the precise network structure is uncertain.
• Demonstrates the utility of the combined structure learning and causal inference approach.
• Provides a viable solution for causal inference problems with complex data dependencies.

Conclusions:

• The developed method offers a significant advancement for causal inference in settings with unknown network dependencies.
• This approach is applicable to various fields where data exhibits interconnectedness and structure uncertainty.
• The findings highlight the importance of accounting for network structure in causal effect estimation.