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 inInternational Journal of Approximate Reasoning
© 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  and we label it as surrogate outcome identifiability. We show that the concept of transportability  provides a sufficient criteria for determining surrogate outcome identifiability for a large class of queries.
Publication in research information system
MetadataShow full item record
Related funder(s)Academy of Finland
Funding program(s)Research profiles, AoF
Additional information about fundingThis 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.
Showing items with similar title or keywords.
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 ...
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 ...
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 ...
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 ...
A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization Chugh, Tinkle; Jin, Yaochu; Miettinen, Kaisa; Hakanen, Jussi; Sindhya, Karthik (Institute of Electrical and Electronics Engineers, 2018)We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed ...