Surrogate outcomes and transportability
Tikka, S., & Karvanen, J. (2019). Surrogate outcomes and transportability. International Journal of Approximate Reasoning, 108. https://doi.org/10.1016/j.ijar.2019.02.007
Published in
International Journal of Approximate ReasoningDate
2019Copyright
© 2019 Elsevier Inc.
Identification of causal effects is one of the most fundamental tasks of causal inference. We consider an identifiability problem where some experimental and observational data are available but neither data alone is sufficient for the identification of the causal effect of interest. Instead of the outcome of interest, surrogate outcomes are measured in the experiments. This problem is a generalization of identifiability using surrogate experiments [1] and we label it as surrogate outcome identifiability. We show that the concept of transportability [2] provides a sufficient criteria for determining surrogate outcome identifiability for a large class of queries.
Publisher
ElsevierISSN Search the Publication Forum
0888-613XKeywords
Publication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/28971132
Metadata
Show full item recordCollections
Related funder(s)
Academy of FinlandFunding program(s)
Research profiles, AoF
Additional information about funding
This work belongs to the thematic research area “Decision analytics utilizing causal models and multiobjective optimization” (DEMO) supported by Academy of Finland (grant number 311877). We thank the anonymous reviewers for their comments which helped to substantially improve this paper.License
Related items
Showing items with similar title or keywords.
-
Identifying Causal Effects with the R Package causaleffect
Tikka, Santtu; Karvanen, Juha (Foundation for Open Access Statistics, 2017)Do-calculus is concerned with estimating the interventional distribution of an action from the observed joint probability distribution of the variables in a given causal structure. All identifiable causal effects can be ... -
Causal Effect Identification from Multiple Incomplete Data Sources : A General Search-Based Approach
Tikka, Santtu; Hyttinen, Antti; Karvanen, Juha (Foundation for Open Access Statistic, 2021)Causal effect identification considers whether an interventional probability distribution can be uniquely determined without parametric assumptions from measured source distributions and structural knowledge on the generating ... -
Optimization of Linearized Belief Propagation for Distributed Detection
Abdi, Younes; Ristaniemi, Tapani (IEEE, 2020)In this paper, we investigate distributed inference schemes, over binary-valued Markov random fields, which are realized by the belief propagation (BP) algorithm. We first show that a decision variable obtained by the BP ... -
When does regression discontinuity design work? Evidence from random election outcomes
Hyytinen, Ari; Meriläinen, Jaakko; Saarimaa, Tuukka; Toivanen, Otto; Tukiainen, Janne (Wiley, 2018)We use elections data in which a large number of ties in vote counts betweencandidates are resolved via a lottery to study the personal incumbency advantage. We benchmark non-experimental regression ... -
Identifying Causal Effects via Context-specific Independence Relations
Tikka, Santtu; Hyttinen, Antti; Karvanen, Juha (Neural Information Processing Systems Foundation, Inc., 2019)Causal effect identification considers whether an interventional probability distribution can be uniquely determined from a passively observed distribution in a given causal structure. If the generating system induces ...