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Identification of Personalized Effects Associated With Causal Pathways.

ABSTRACT: Unlike classical causal inference, where the goal is to estimate average causal effects within a population, in settings such as personalized medicine, the goal is to map a unit's characteristics to a treatment tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic treatment regime literature. In healthcare settings, optimizing policies with respect to a particular causal pathway is often of interest as well. In the context of average treatment effects, estimation of effects associated with causal pathways is considered in the mediation analysis literature. In this paper, we combine mediation analysis and dynamic treatment regime ideas and consider how unit characteristics may be used to tailor a treatment strategy that maximizes an effect along specified sets of causal pathways. In particular, we define counterfactual responses to such policies, give a general identification algorithm for these counterfactuals, and prove completeness of the algorithm for unrestricted policies. A corollary of our results is that the identification algorithm for responses to policies given in [16] is complete for arbitrary policies.

SUBMITTER: Shpitser I

PROVIDER: S-EPMC6764091 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Identification of Personalized Effects Associated With Causal Pathways.

Shpitser Ilya I Sherman Eli E

Uncertainty in artificial intelligence : proceedings of the ... conference. Conference on Uncertainty in Artificial Intelligence 20180801

Unlike classical causal inference, where the goal is to estimate average causal effects within a <i>population</i>, in settings such as personalized medicine, the goal is to map a unit's characteristics to a treatment tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic treatment regime literature. In healthcare settings, optimizing policies with respect to a particular causal pathway is often of interest as well. In the ...[more]

PMID: 31565035

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Project description:Unraveling the molecular processes that lead from genotype to phenotype is crucial for the understanding and effective treatment of genetic diseases. Knowledge of the causative genetic defect most often does not enable treatment; therefore, causal intermediates between genotype and phenotype constitute valuable candidates for molecular intervention points that can be therapeutically targeted. Mapping genetic determinants of gene expression levels (also known as expression quantitative trait loci or eQTL studies) is frequently used for this purpose, yet distinguishing causation from correlation remains a significant challenge. Here, we address this challenge using extensive, multi-environment gene expression and fitness profiling of hundreds of genetically diverse yeast strains, in order to identify truly causal intermediate genes that condition fitness in a given environment. Using functional genomics assays, we show that the predictive power of eQTL studies for inferring causal intermediate genes is poor unless performed across multiple environments. Surprisingly, although the effects of genotype on fitness depended strongly on environment, causal intermediates could be most reliably predicted from genetic effects on expression present in all environments. Our results indicate a mechanism explaining this apparent paradox, whereby immediate molecular consequences of genetic variation are shared across environments, and environment-dependent phenotypic effects result from downstream integration of environmental signals. We developed a statistical model to predict causal intermediates that leverages this insight, yielding over 400 transcripts, for the majority of which we experimentally validated their role in conditioning fitness. Our findings have implications for the design and analysis of clinical omics studies aimed at discovering personalized targets for molecular intervention, suggesting that inferring causation in a single cellular context can benefit from molecular profiling in multiple contexts.

Dataset Information

Identification of Personalized Effects Associated With Causal Pathways.

Publications

Identification of Personalized Effects Associated With Causal Pathways.

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