Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach
Dublin Core
Title
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach
Subject
causality, do-calculus, selection bias, transportability, missing data, case-control
design, meta-analysis
design, meta-analysis
Description
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.
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.
Creator
Santtu Tikka
Source
https://www.jstatsoft.org/article/view/v099i05
Publisher
University of Jyvaskyla
Date
August 2021
Contributor
Fajar bagus W
Format
PDF
Language
Inggris
Type
Text
Files
Collection
Citation
Santtu Tikka, “Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8206.