skip to main content
10.1145/2939672.2939791acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

Inferring Network Effects from Observational Data

Published: 13 August 2016 Publication History

Abstract

We present Relational Covariate Adjustment (RCA), a general method for estimating causal effects in relational data. Relational Covariate Adjustment is implemented through two high-level operations: identification of an adjustment set and relational regression adjustment. The former is achieved through an extension of Pearl's back-door criterion to relational domains. We demonstrate how this extended definition can be used to estimate causal effects in the presence of network interference and confounding. RCA is agnostic to functional form, and it can easily model both discrete and continuous treatments as well as estimate the effects of a wider array of network interventions than existing experimental approaches. We show that RCA can yield robust estimates of causal effects using common regression models without extensive parameter tuning. Through a series of simulation experiments on a variety of synthetic and real-world network structures, we show that causal effects estimated on observational data with RCA are nearly as accurate as those estimated from well-designed network experiments

Supplementary Material

MP4 File (kdd2016_arbour_network_effects_01-acm.mp4)

References

[1]
S. Aral and D. Walker. Creating social contagion through viral product design: A randomized trial of peer influence in networks. Management Science, 57(9):1623--1639, 2011.
[2]
P. M. Aronow and C. Samii. Estimating average causal effects under general interference. In Summer Meeting of the Society for Political Methodology, University of North Carolina, Chapel Hill, July, pages 19--21, 2012.
[3]
E. Bakshy, D. Eckles, and M. S. Bernstein. Designing and deploying online field experiments. In Proceedings of the 23rd international conference on World wide, web, pages 283--292. ACM, 2014.
[4]
E. Bakshy, D. Eckles, R. Yan, and I. Rosenn. Social influence in social advertising: Evidence from field experiments. In Proceedings of the 13th ACM Conference on Electronic Commerce, pages 146--161. ACM, 2012.
[5]
J. Bosch. Building products as innovation experiment systems. In Software Business, pages 27--39. Springer, 2012.
[6]
D. S. Choi. Estimation of monotone treatment effects in network experiments. arXiv preprint arXiv:1408.4102, 2014.
[7]
J. H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, pages 1189--1232, 2001.
[8]
H. Gui, Y. Xu, A. Bhasin, and J. Han. Network a/b testing: From sampling to estimation. In Proceedings of the 24th International Conference on World Wide Web, pages 399--409. International World Wide Web Conferences Steering Committee, 2015.
[9]
M. G. Hudgens and M. E. Halloran. Toward causal inference with interference. Journal of the American Statistical Association, 2012.
[10]
M. Kearns, S. Judd, and Y. Vorobeychik. Behavioral experiments on a network formation game. In Proceedings of the 13th ACM Conference on Electronic Commerce, pages 690--704. ACM, 2012.
[11]
R. Kohavi, A. Deng, B. Frasca, T. Walker, Y. Xu, and N. Pohlmann. Online controlled experiments at large scale. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1168--1176. ACM, 2013.
[12]
D. Koller. Probabilistic relational models. In Inductive logic programming, pages 3--13. Springer, 1999.
[13]
S. S. Krishnan and R. K. Sitaraman. Video stream quality impacts viewer behavior: Inferring causality using quasi-experimental designs. Networking, IEEE/ACM Transactions on, 21(6):2001--2014, 2013.
[14]
J. Leskovec and A. Krevl. SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data, June 2014.
[15]
M. Maier, K. Marazopoulou, D. Arbour, and D. Jensen. A sound and complete algorithm for learning causal models from relational data. In Uncertainty in Artificial Intelligence, page 371. Citeseer, 2013.
[16]
M. E. Maier. Causal Discovery for Relational Domains: Representation, Reasoning, and Learning. PhD thesis, University of Massachusetts Amherst, 2014.
[17]
M. E. Maier, K. Marazopoulou, and D. D. Jensen. Reasoning about independence in probabilistic models of relational data. arXiv preprint arXiv:1302.4381, 2014.
[18]
C. F. Manski. Identification of treatment response with social interactions. The Econometrics Journal, 16(1):S1--S23, 2013.
[19]
E. L. Ogburn and T. J. VanderWeele. Causal diagrams for interference. Statist. Sci., 29(4):559--578, 11 2014.
[20]
Oktay, B. J. Taylor, and D. Jensen. Causal discovery in social media using quasi-experimental designs. In Proceedings of the SIGKDD/ACM Workshop on Social Media Analytics, 2010.
[21]
J. Pearl. Graphs, causality, and structural equation models. Sociological Methods & Research, 27(2):226--284, 1998.
[22]
J. Pearl. Causality. Cambridge university press, 2009.
[23]
C. Perlich and F. Provost. Distribution-based aggregation for relational learning with identifier attributes. Machine Learning, 62(1-2):65--105, 2006.
[24]
D. B. Rubin. Matching to remove bias in observational studies. Biometrics, pages 159--183, 1973.
[25]
D. B. Rubin. Causal inference using potential outcomes. Journal of the American Statistical Association, 2011.
[26]
P. Toulis and E. Kao. Estimation of causal peer influence effects. In Proceedings of The 30th International Conference on Machine Learning, pages 1489--1497, 2013.
[27]
J. Ugander, B. Karrer, L. Backstrom, and J. Kleinberg. Graph cluster randomization: Network exposure to multiple universes. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 329--337. ACM, 2013.

Cited By

View all
  • (2025)Networked Instrumental Variable for Treatment Effect Estimation With Unobserved ConfoundersIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.349177637:2(823-836)Online publication date: Feb-2025
  • (2024)Causal Graph Representation Learning for Outcome-Oriented Link Prediction2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651266(1-8)Online publication date: 30-Jun-2024
  • (2024)Uplift Modeling Under Limited SupervisionMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70365-2_8(127-144)Online publication date: 22-Aug-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 2016
2176 pages
ISBN:9781450342322
DOI:10.1145/2939672
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 August 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. causality
  2. relational learning

Qualifiers

  • Research-article

Conference

KDD '16
Sponsor:

Acceptance Rates

KDD '16 Paper Acceptance Rate 66 of 1,115 submissions, 6%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)23
  • Downloads (Last 6 weeks)1
Reflects downloads up to 17 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2025)Networked Instrumental Variable for Treatment Effect Estimation With Unobserved ConfoundersIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.349177637:2(823-836)Online publication date: Feb-2025
  • (2024)Causal Graph Representation Learning for Outcome-Oriented Link Prediction2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651266(1-8)Online publication date: 30-Jun-2024
  • (2024)Uplift Modeling Under Limited SupervisionMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70365-2_8(127-144)Online publication date: 22-Aug-2024
  • (2023)CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical SystemsProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599272(997-1009)Online publication date: 6-Aug-2023
  • (2023)Efficient Estimation of Local Causal Effects in Graphs via Neighborhood Pooling2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386978(542-547)Online publication date: 15-Dec-2023
  • (2023)Disentangling causality: assumptions in causal discovery and inferenceArtificial Intelligence Review10.1007/s10462-023-10411-956:9(10613-10649)Online publication date: 27-Feb-2023
  • (2022)Learning Causal Effects on HypergraphsProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539299(1202-1212)Online publication date: 14-Aug-2022
  • (2022)Estimating Causal Effects on Networked Observational Data via Representation LearningProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557311(852-861)Online publication date: 17-Oct-2022
  • (2022)Data-driven causal knowledge graph construction for root cause analysis in quality problem solvingInternational Journal of Production Research10.1080/00207543.2022.207874861:10(3227-3245)Online publication date: 27-May-2022
  • (2021)Causal Inference from Network DataProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3470795(4096-4097)Online publication date: 14-Aug-2021
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media