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pro vyhledávání: '"Airoldi Edoardo M"'
Interference is ubiquitous when conducting causal experiments over networks. Except for certain network structures, causal inference on the network in the presence of interference is difficult due to the entanglement between the treatment assignments
Externí odkaz:
http://arxiv.org/abs/2312.04026
Autor:
Karwa, Vishesh, Airoldi, Edoardo M.
The Horvitz-Thompson (H-T) estimator is widely used for estimating various types of average treatment effects under network interference. We systematically investigate the optimality properties of H-T estimator under network interference, by embeddin
Externí odkaz:
http://arxiv.org/abs/2312.01234
The estimation of heterogeneous treatment effects in the potential outcome setting is biased when there exists model misspecification or unobserved confounding. As these biases are unobservable, what model to use when remains a critical open question
Externí odkaz:
http://arxiv.org/abs/2304.07726
Randomized experiments are widely used to estimate the causal effects of a proposed treatment in many areas of science, from medicine and healthcare to the physical and biological sciences, from the social sciences to engineering, to public policy an
Externí odkaz:
http://arxiv.org/abs/2205.12803
Randomized saturation designs are a family of designs which assign a possibly different treatment proportion to each cluster of a population at random. As a result, they generalize the well-known (stratified) completely randomized designs and the clu
Externí odkaz:
http://arxiv.org/abs/2203.09682
Akademický článek
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Publikováno v:
Proc. Natl. Acad. Sci. USA 117(38), 23393-23400 (2020)
Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speedup the collection of network data and improve the validity of network models. Many algorithms now exist for predi
Externí odkaz:
http://arxiv.org/abs/1909.07578
Some of the most used sampling mechanisms that implicitly leverage a social network depend on tuning parameters; for instance, Respondent-Driven Sampling (RDS) is specified by the number of seeds and maximum number of referrals. We are interested in
Externí odkaz:
http://arxiv.org/abs/1811.07829
Autor:
Karwa, Vishesh, Airoldi, Edoardo M.
We systematically investigate issues due to mis-specification that arise in estimating causal effects when (treatment) interference is informed by a network available pre-intervention, i.e., in situations where the outcome of a unit may depend on the
Externí odkaz:
http://arxiv.org/abs/1810.08259