Treatment effect estimation with observational network data using machine learning
Causal inference methods for treatment effect estimation usually assume independent units. However, this assumption is often questionable because units may interact, resulting in spillover effects between them. We develop augmented inverse probability weighting (AIPW) for estimation and inference of...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-04-01
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| Series: | Journal of Causal Inference |
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| Online Access: | https://doi.org/10.1515/jci-2023-0082 |
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| author | Emmenegger Corinne Spohn Meta-Lina Elmer Timon Bühlmann Peter |
| author_facet | Emmenegger Corinne Spohn Meta-Lina Elmer Timon Bühlmann Peter |
| author_sort | Emmenegger Corinne |
| collection | DOAJ |
| description | Causal inference methods for treatment effect estimation usually assume independent units. However, this assumption is often questionable because units may interact, resulting in spillover effects between them. We develop augmented inverse probability weighting (AIPW) for estimation and inference of the expected average treatment effect (EATE) with observational data from a single (social) network with spillover effects. In contrast to overall effects such as the global average treatment effect, the EATE measures, in expectation and on average over all units, how the outcome of a unit is causally affected by its own treatment, marginalizing over the spillover effects from other units. We develop cross-fitting theory with plugin machine learning to obtain a semiparametric treatment effect estimator that converges at the parametric rate and asymptotically follows a Gaussian distribution. The asymptotics are developed using the dependency graph rather than the network graph, which makes explicit that we allow for spillover effects beyond immediate neighbors in the network. We apply our AIPW method to the Swiss StudentLife Study data to investigate the effect of hours spent studying on exam performance accounting for the students’ social network. |
| format | Article |
| id | doaj-art-25ec5c8be1584caaa3b836f9775fe7e3 |
| institution | OA Journals |
| issn | 2193-3685 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Causal Inference |
| spelling | doaj-art-25ec5c8be1584caaa3b836f9775fe7e32025-08-20T01:54:19ZengDe GruyterJournal of Causal Inference2193-36852025-04-01131591310.1515/jci-2023-0082Treatment effect estimation with observational network data using machine learningEmmenegger Corinne0Spohn Meta-Lina1Elmer Timon2Bühlmann Peter3Seminar for Statistics, ETH Zurich, Zurich, SwitzerlandSeminar for Statistics, ETH Zurich, Zurich, SwitzerlandDepartment of Humanities, Social and Political Sciences, ETH Zurich, Zurich, SwitzerlandSeminar for Statistics, ETH Zurich, Zurich, SwitzerlandCausal inference methods for treatment effect estimation usually assume independent units. However, this assumption is often questionable because units may interact, resulting in spillover effects between them. We develop augmented inverse probability weighting (AIPW) for estimation and inference of the expected average treatment effect (EATE) with observational data from a single (social) network with spillover effects. In contrast to overall effects such as the global average treatment effect, the EATE measures, in expectation and on average over all units, how the outcome of a unit is causally affected by its own treatment, marginalizing over the spillover effects from other units. We develop cross-fitting theory with plugin machine learning to obtain a semiparametric treatment effect estimator that converges at the parametric rate and asymptotically follows a Gaussian distribution. The asymptotics are developed using the dependency graph rather than the network graph, which makes explicit that we allow for spillover effects beyond immediate neighbors in the network. We apply our AIPW method to the Swiss StudentLife Study data to investigate the effect of hours spent studying on exam performance accounting for the students’ social network.https://doi.org/10.1515/jci-2023-0082dependent datainterferenceobserved confoundingsemiparametric inferencespillover effects62d2062g20 |
| spellingShingle | Emmenegger Corinne Spohn Meta-Lina Elmer Timon Bühlmann Peter Treatment effect estimation with observational network data using machine learning Journal of Causal Inference dependent data interference observed confounding semiparametric inference spillover effects 62d20 62g20 |
| title | Treatment effect estimation with observational network data using machine learning |
| title_full | Treatment effect estimation with observational network data using machine learning |
| title_fullStr | Treatment effect estimation with observational network data using machine learning |
| title_full_unstemmed | Treatment effect estimation with observational network data using machine learning |
| title_short | Treatment effect estimation with observational network data using machine learning |
| title_sort | treatment effect estimation with observational network data using machine learning |
| topic | dependent data interference observed confounding semiparametric inference spillover effects 62d20 62g20 |
| url | https://doi.org/10.1515/jci-2023-0082 |
| work_keys_str_mv | AT emmeneggercorinne treatmenteffectestimationwithobservationalnetworkdatausingmachinelearning AT spohnmetalina treatmenteffectestimationwithobservationalnetworkdatausingmachinelearning AT elmertimon treatmenteffectestimationwithobservationalnetworkdatausingmachinelearning AT buhlmannpeter treatmenteffectestimationwithobservationalnetworkdatausingmachinelearning |