Zobrazeno 1 - 10
of 124
pro vyhledávání: '"Ugander Johan"'
Autor:
Ugander Johan, Yin Hao
Publikováno v:
Journal of Causal Inference, Vol 11, Iss 1, Pp 832-42 (2023)
The global average treatment effect (GATE) is a primary quantity of interest in the study of causal inference under network interference. With a correctly specified exposure model of the interference, the Horvitz–Thompson (HT) and Hájek estimators
Externí odkaz:
https://doaj.org/article/6ba80024e7844b95965e18515e889d0b
Autor:
Khan Samir, Ugander Johan
Publikováno v:
Journal of Causal Inference, Vol 11, Iss 1, Pp 663-85 (2023)
Inverse probability weighting (IPW) is a general tool in survey sampling and causal inference, used in both Horvitz–Thompson estimators, which normalize by the sample size, and Hájek/self-normalized estimators, which normalize by the sum of the in
Externí odkaz:
https://doaj.org/article/83981a401a78408f86cc1e557294e534
Autor:
Han Kevin, Ugander Johan
Publikováno v:
Journal of Causal Inference, Vol 11, Iss 1, Pp 688-10 (2023)
When estimating a global average treatment effect (GATE) under network interference, units can have widely different relationships to the treatment depending on a combination of the structure of their network neighborhood, the structure of the interf
Externí odkaz:
https://doaj.org/article/155d3a76b88f49fba2b4d1bd18dc863f
Autor:
Awadelkarim, Amel, Ugander, Johan
In many contexts involving ranked preferences, agents submit partial orders over available alternatives. Statistical models often treat these as marginal in the space of total orders, but this approach overlooks information contained in the list leng
Externí odkaz:
http://arxiv.org/abs/2406.15893
Variance reduction for causal inference in the presence of network interference is often achieved through either outcome modeling, which is typically analyzed under unit-randomized Bernoulli designs, or clustered experimental designs, which are typic
Externí odkaz:
http://arxiv.org/abs/2405.07979
A wide range of graph embedding objectives decompose into two components: one that attracts the embeddings of nodes that are perceived as similar, and another that repels embeddings of nodes that are perceived as dissimilar. Because real-world graphs
Externí odkaz:
http://arxiv.org/abs/2405.00172
Publikováno v:
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:6202-6252, 2024
A common network inference problem, arising from real-world data constraints, is how to infer a dynamic network from its time-aggregated adjacency matrix and time-varying marginals (i.e., row and column sums). Prior approaches to this problem have re
Externí odkaz:
http://arxiv.org/abs/2402.18697
A core tension in the study of plurality elections is the clash between the classic Hotelling-Downs model, which predicts that two office-seeking candidates should position themselves at the median voter's policy, and the empirical observation that r
Externí odkaz:
http://arxiv.org/abs/2402.17109
Although the foundations of ranking are well established, the ranking literature has primarily been focused on simple, unimodal models, e.g. the Mallows and Plackett-Luce models, that define distributions centered around a single total ordering. Expl
Externí odkaz:
http://arxiv.org/abs/2312.15081
Publikováno v:
Journal of Causal Inference, Vol 5, Iss 1, Pp 115-136 (2016)
Estimating the effects of interventions in networks is complicated due to interference, such that the outcomes for one experimental unit may depend on the treatment assignments of other units. Familiar statistical formalism, experimental designs, and
Externí odkaz:
https://doaj.org/article/f3acb6779c5e435da210473f24a097f1