Neighborhood Interference Models

Neighborhood Interference Models is a statistical framework that belongs to the class of causal inference models and is distinguished by its explicit incorporation of spatial or network-based dependencies to account for interference effects occurring among neighboring units in observational or experimental studies.

12

Publications

1.1K

Citations

19

Authors

17

Institutions

Publications per year

2017–2026

2

Authors

19

  1. SW
    Stephen W. Pacala

    Princeton University, United States

    3 publications · 395 citations

    3

    h-index

  2. DE
    Dean Eckles

    Massachusetts Institute of Technology, United States

    2 publications · 264 citations

    2

    h-index

  3. BK
    Brian Karrer

    University of Michigan, United States

    2 publications · 264 citations

    2

    h-index

  4. JA
    John A. Silander

    University of Connecticut, United States

    2 publications · 290 citations

    2

    h-index

  5. JU
    Johan Ugander

    Stanford University, United States

    2 publications · 264 citations

    2

    h-index

1–5 of 19

Venues

  1. RePEc: Research Papers in Economics

    Repository · 1 publications · 214 citations

    2

    h-index

  2. Weed Science

    1 publications · 44 citations

    1

    h-index

  3. Oikos

    1 publications · 79 citations

    1

    h-index

  4. Journal of Causal Inference

    1 publications · 50 citations

    1

    h-index

  5. Future Generation Computer Systems

    FGCS · 1 publications · 15 citations

    1

    h-index

1–5