Detecting Treatment Effect Modifiers in Social Networks. Article Swipe
We study treatment effect modifiers for causal analysis in a social network, where neighbors' characteristics or network structure may affect the outcome of a unit, and the goal is to identify sub-populations with varying treatment effects using such network properties. We propose a novel framework called Testing-for-Effect-Modifier (TEEM) for this purpose that facilitates data-driven decision making by testing hypotheses about complex effect modifiers in terms of network features or network patterns (e.g., characteristics of neighbors of a unit or belonging to a triangle), and by identifying sub-populations for which a treatment is likely to be effective or harmful. We describe a hypothesis testing approach that accounts for unit's covariates, their neighbors' covariates and patterns in the social network, and devise an algorithm incorporating ideas from causal inference, hypothesis testing, and graph theory to verify a hypothesized effect modifier. We perform extensive experimental evaluations with a real development economics dataset about the treatment effect of belonging to a financial support network called self-help groups on risk tolerance, and also with a synthetic dataset with known ground truths simulating a vaccine efficacy trial, to evaluate our framework and algorithms.
Related Topics
Concepts
Causal inference
Covariate
Inference
Computer science
Social network (sociolinguistics)
Outcome (game theory)
Machine learning
Graph
Affect (linguistics)
Artificial intelligence
Causal structure
Econometrics
Data mining
Psychology
Theoretical computer science
Mathematics
Microeconomics
Economics
Communication
Social media
Physics
Quantum mechanics
World Wide Web
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://arxiv.org/abs/2105.10591
- OA Status
- green
- References
- 44
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3164099963
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3164099963Canonical identifier for this work in OpenAlex
- Title
-
Detecting Treatment Effect Modifiers in Social Networks.Work title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-05-21Full publication date if available
- Authors
-
Amir Gilad, Harsh Parikh, Babak Salimi, Sudeepa RoyList of authors in order
- Landing page
-
https://arxiv.org/abs/2105.10591Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/abs/2105.10591Direct OA link when available
- Concepts
-
Causal inference, Covariate, Inference, Computer science, Social network (sociolinguistics), Outcome (game theory), Machine learning, Graph, Affect (linguistics), Artificial intelligence, Causal structure, Econometrics, Data mining, Psychology, Theoretical computer science, Mathematics, Microeconomics, Economics, Communication, Social media, Physics, Quantum mechanics, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
44Number of works referenced by this work
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.where | 12 |
| abstract_inverted_index.which | 88 |
| abstract_inverted_index.(TEEM) | 47 |
| abstract_inverted_index.(e.g., | 71 |
| abstract_inverted_index.affect | 19 |
| abstract_inverted_index.called | 45, 160 |
| abstract_inverted_index.causal | 6, 125 |
| abstract_inverted_index.devise | 119 |
| abstract_inverted_index.effect | 3, 61, 136, 152 |
| abstract_inverted_index.ground | 174 |
| abstract_inverted_index.groups | 162 |
| abstract_inverted_index.likely | 92 |
| abstract_inverted_index.making | 55 |
| abstract_inverted_index.social | 10, 116 |
| abstract_inverted_index.theory | 131 |
| abstract_inverted_index.trial, | 180 |
| abstract_inverted_index.truths | 175 |
| abstract_inverted_index.unit's | 107 |
| abstract_inverted_index.verify | 133 |
| abstract_inverted_index.complex | 60 |
| abstract_inverted_index.dataset | 148, 171 |
| abstract_inverted_index.effects | 35 |
| abstract_inverted_index.network | 16, 38, 66, 69, 159 |
| abstract_inverted_index.outcome | 21 |
| abstract_inverted_index.perform | 139 |
| abstract_inverted_index.propose | 41 |
| abstract_inverted_index.purpose | 50 |
| abstract_inverted_index.support | 158 |
| abstract_inverted_index.testing | 57, 102 |
| abstract_inverted_index.vaccine | 178 |
| abstract_inverted_index.varying | 33 |
| abstract_inverted_index.accounts | 105 |
| abstract_inverted_index.analysis | 7 |
| abstract_inverted_index.approach | 103 |
| abstract_inverted_index.decision | 54 |
| abstract_inverted_index.describe | 99 |
| abstract_inverted_index.efficacy | 179 |
| abstract_inverted_index.evaluate | 182 |
| abstract_inverted_index.features | 67 |
| abstract_inverted_index.harmful. | 97 |
| abstract_inverted_index.identify | 30 |
| abstract_inverted_index.network, | 11, 117 |
| abstract_inverted_index.patterns | 70, 113 |
| abstract_inverted_index.testing, | 128 |
| abstract_inverted_index.algorithm | 121 |
| abstract_inverted_index.belonging | 79, 154 |
| abstract_inverted_index.economics | 147 |
| abstract_inverted_index.effective | 95 |
| abstract_inverted_index.extensive | 140 |
| abstract_inverted_index.financial | 157 |
| abstract_inverted_index.framework | 44, 184 |
| abstract_inverted_index.modifier. | 137 |
| abstract_inverted_index.modifiers | 4, 62 |
| abstract_inverted_index.neighbors | 74 |
| abstract_inverted_index.self-help | 161 |
| abstract_inverted_index.structure | 17 |
| abstract_inverted_index.synthetic | 170 |
| abstract_inverted_index.treatment | 2, 34, 90, 151 |
| abstract_inverted_index.covariates | 111 |
| abstract_inverted_index.hypotheses | 58 |
| abstract_inverted_index.hypothesis | 101, 127 |
| abstract_inverted_index.inference, | 126 |
| abstract_inverted_index.neighbors' | 13, 110 |
| abstract_inverted_index.simulating | 176 |
| abstract_inverted_index.tolerance, | 165 |
| abstract_inverted_index.triangle), | 82 |
| abstract_inverted_index.algorithms. | 186 |
| abstract_inverted_index.covariates, | 108 |
| abstract_inverted_index.data-driven | 53 |
| abstract_inverted_index.development | 146 |
| abstract_inverted_index.evaluations | 142 |
| abstract_inverted_index.facilitates | 52 |
| abstract_inverted_index.identifying | 85 |
| abstract_inverted_index.properties. | 39 |
| abstract_inverted_index.experimental | 141 |
| abstract_inverted_index.hypothesized | 135 |
| abstract_inverted_index.incorporating | 122 |
| abstract_inverted_index.characteristics | 14, 72 |
| abstract_inverted_index.sub-populations | 31, 86 |
| abstract_inverted_index.Testing-for-Effect-Modifier | 46 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |