Causal Effect Identification from Multiple Incomplete Data Sources : A General Search-Based Approach
Tikka, S., Hyttinen, A., & Karvanen, J. (2021). Causal Effect Identification from Multiple Incomplete Data Sources : A General Search-Based Approach. Journal of Statistical Software, 99, Article 5. https://doi.org/10.18637/jss.v099.i05
Published inJournal of Statistical Software
© Authors, 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 system. While complete graphical criteria and procedures exist for many identification problems, there are still challenging but important extensions that have not been considered in the literature such as combined transportability and selection bias, or multiple sources of selection bias. To tackle these new settings, we present a search algorithm directly over the rules of do-calculus. Due to the generality of do-calculus, the search is capable of taking more advanced datagenerating mechanisms into account along with an arbitrary type of both observational and experimental source distributions. The search is enhanced via a heuristic and search space reduction techniques. The approach, called do-search, is provably sound, and it is complete with respect to identifiability problems that have been shown to be completely characterized by do-calculus. When extended with additional rules, the search is capable of handling missing data problems as well. With the versatile search, we are able to approach new problems for which no other algorithmic solutions exist. We perform a systematic analysis of bivariate missing data problems and study causal inference under case-control design. We also present the R package dosearch that provides an interface for a C++ implementation of the search. ...
PublisherFoundation for Open Access Statistic
Publication in research information system
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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). AH was supported by Academy of Finland through grant 295673.
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