Translating literature into causal graphs: Toward automated experiment selection

Matiasz, Nicholas J.; Wood, Joanne V.; Wang, Wei; Silva, Alcino J.; Hsu, William

doi:10.1109/bibm.2017.8217713

Cited by 2 publications

(8 citation statements)

References 10 publications

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“…Fig. 1, adapted from [10], provides an overview of the proposed methods. Because of the constraint-based causal discovery algorithm that we use, our approach can readily accommodate the background knowledge from a domain expert [6].…”

Section: Methodsmentioning

confidence: 99%

“…The algorithm proceeds in the following steps. First, (in)dependence relations among the system's variables are obtained-either by performing statistical independence tests on the data [15], or by annotating statistical relations that are reported in the literature, as is done with the ResearchMaps web application [8]- [10]. If none of the constraints conflict with each other, then a Boolean satisfiability (SAT) solver [19] is sufficient to find the consistent causal graphs [20].…”

Section: Constraint-based Causal Discoverymentioning

confidence: 99%

“…The degrees of freedom for a causal graph are the possible variations in edge relations that can exist between any two variables throughout an equivalence class [10]. For DAGs, these edge relations are:…”

Section: Degrees Of Freedommentioning

confidence: 99%

“…Next, these annotations are input to an algorithm that identifies the causal graphs consistent with the annotated results. The scientist can then inspect the consistent graphs to see which inferences arise out of the synthesis of annotated research articles [9], [10].…”

Section: Introductionmentioning

confidence: 99%

“…We characterize this underdetermination with a causal graph's degrees of freedom, which represent the diversity of edge relations that appear in the graphs of an equivalence class [9], [10]. For example, all graphs in an equivalence class may have the same edge relation between the variables X and Y (e.g., X → Y ), but there may be a diversity of edge relations between the variables Y and Z (e.g., Y ← Z and Y → Z).…”

Section: Introductionmentioning

confidence: 99%

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Experiment Selection in Meta-Analytic Piecemeal Causal Discovery

et al. 2021

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Scientists try to design experiments that will yield maximal information. For instance, given the available evidence and a limitation on the number of variables that can be observed simultaneously, it may be more informative to intervene on variable X and observe the response of variable Y than to intervene on X and observe Z; in other situations, the opposite may be true. Scientists must often make these decisions without primary data. To address this problem, in previous work, we created software for annotating aggregate statistics in the literature and deriving consistent causal explanations, expressed as causal graphs. This meta-analytic pipeline is useful not only for synthesizing evidence but also for planning experiments: one can use it strategically to select experiments that could further eliminate causal graphs from consideration. In this paper, we introduce interpretable policies for selecting experiments in the context of piecemeal causal discovery, a common setting in biological sciences in which each experiment can measure not an entire system but rather a strict subset of its variables. The limits of this piecemeal approach are only beginning to be fully characterized, with crucial theoretical work published recently [1]- [3]. With simulations, we show that our experiment-selection policies identify causal structures more efficiently than random experiment selection. Unlike methods that require primary data, our meta-analytic approach offers a flexible alternative for those seeking to incorporate qualitative domain knowledge into their search for causal mechanisms. We also present a method that categorizes hypotheses with respect to their utility for identifying a system's causal structure. Although this categorization is usually infeasible to perform manually, it is critical for conducting research efficiently.

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Section: Methodsmentioning

confidence: 99%

Section: Constraint-based Causal Discoverymentioning

confidence: 99%

Section: Degrees Of Freedommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Experiment Selection in Meta-Analytic Piecemeal Causal Discovery

et al. 2021

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ResearchMaps.org for integrating and planning research

et al. 2018

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To plan experiments, a biologist needs to evaluate a growing set of empirical findings and hypothetical assertions from diverse fields that use increasingly complex techniques. To address this problem, we operationalized principles (e.g., convergence and consistency) that biologists use to test causal relations and evaluate experimental evidence. With the framework we derived, we then created a free, open-source web application that allows biologists to create research maps, graph-based representations of empirical evidence and hypothetical assertions found in research articles, reviews, and other sources. With our ResearchMaps web application, biologists can systematically reason through the research that is most important to them, as well as evaluate and plan experiments with a breadth and precision that are unlikely without such a tool.

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Translating literature into causal graphs: Toward automated experiment selection

Cited by 2 publications

References 10 publications

Experiment Selection in Meta-Analytic Piecemeal Causal Discovery

Experiment Selection in Meta-Analytic Piecemeal Causal Discovery

ResearchMaps.org for integrating and planning research

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