Surrogate outcomes and transportability

Tikka, Santtu; Karvanen, Juha

doi:10.1016/j.ijar.2019.02.007

Cited by 7 publications

(8 citation statements)

References 7 publications

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“…Earlier generalizations of the identifiability problem assume nested experiments or entire distributions with the exception of surrogate outcome identifiability (Tikka and Karvanen 2019) which also has its own intricate set of assumptions regarding the available distributions. None of these assumptions are needed in do-search and it can be used to solve identifiability problems from completely arbitrary collections of input distributions.…”

Section: Multiple Data Sources With Partially Overlapping Variable Setsmentioning

confidence: 99%

Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach

Tikka¹,

Hyttinen²,

Karvanen³

2021

J. Stat. Soft.

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Section: Multiple Data Sources With Partially Overlapping Variable Setsmentioning

confidence: 99%

Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach

Tikka¹,

Hyttinen²,

Karvanen³

2021

J. Stat. Soft.

Self Cite

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“…Docalculus consists of rules for inserting and deleting observations, exchanging observations and interventions, and inserting and deleting interventions. There exists efficient algo-rithms for determining identifiability for settings where data from a single observational source (Shpitser and Pearl, 2006), from multiple domains (Bareinboim and Pearl, 2013), or from surrogate experiments (Bareinboim and Pearl, 2012;Tikka and Karvanen, 2019;Lee et al, 2019) are available. An open-source software implementation for many of these algorithms are available as well (Tikka and Karvanen, 2017).…”

Section: Concepts and Notationmentioning

confidence: 99%

Do-search -- a tool for causal inference and study design with multiple data sources

Karvanen,

Tikka,

Hyttinen

2020

Preprint

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Epidemiological evidence is based on multiple data sources including clinical trials, cohort studies, surveys, registries and expert opinions. Merging information from different sources opens up new possibilities for the estimation of causal effects. We show how causal effects can be identified and estimated by combining experiments and observations in real and realistic scenarios. As a new tool, we present do-search, a recently developed algorithmic approach that can determine the identifiability of a causal effect. The approach is based on do-calculus, and it can utilize data with non-trivial missing data and selection bias mechanisms. When the effect is identifiable, do-search outputs an identifying formula on which numerical estimation can be based. When the effect is not identifiable, we can use do-search to recognize additional data sources and assumptions that would make the effect identifiable. Throughout the paper, we consider the effect of salt-adding behavior on blood pressure mediated by the salt intake as an example. The identifiability of this effect is resolved in various scenarios with different assumptions on confounding. There are scenarios where the causal effect is identifiable from a chain of experiments but not from survey data, as well as scenarios where the opposite is true. As an illustration, we use survey data from NHANES 2013-2016 and the results from a meta-analysis of randomized controlled trials and estimate the reduction in average systolic blood pressure under an intervention where the use of table salt is discontinued.

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“…The assumptions of nested experiments (NE), entire distributions (ED) and nested experiments in different domains (NEDD) are explained in Section 2. Assumptions related to surrogate outcomes (SO) can be found in (Tikka and Karvanen 2019). Input P (V | S) means the joint distribution under selection bias.…”

Section: Arbitrary Do-search (?) With Missing Datamentioning

confidence: 99%

“…For surrogate outcomes, the assumption of nested experiments still holds, but the assumption of entire distributions can be dropped. Some less strict assumptions (SO) still apply (Tikka and Karvanen 2019). The idea of surrogate outcomes is that data from previous experiments are available, but the target Y was at most only partially measured in these experiments and the experiments do not have to be disjoint from X.…”

Section: Surrogate Outcome Identifiabilitymentioning

confidence: 99%

“…Earlier generalizations of the identifiability problem assume nested experiments or entire q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q 0.01 distributions with the exception of surrogate outcome identifiability (Tikka and Karvanen 2019) which also has its own intricate assumptions regarding the available distributions. None of these assumptions are needed in do-search and it can be used to solve identifiability problems from completely arbitrary collections of input distributions.…”

Section: Multiple Data Sources With Partially Overlapping Variable Setsmentioning

confidence: 99%

See 1 more Smart Citation

Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach

Tikka,

Hyttinen,

Karvanen

2019

Preprint

Self Cite

View full text Add to dashboard Cite

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. To tackle these new settings, we present a search algorithm directly over the rules of do-calculus. Due to 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 such as combined transportability and selection bias, or multiple sources of selection bias. 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.

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Surrogate outcomes and transportability

Cited by 7 publications

References 7 publications

Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach

Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-Based Approach

Do-search -- a tool for causal inference and study design with multiple data sources

Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach

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