7 research outputs found
Learning sources of variability from high-dimensional observational studies
Causal inference studies whether the presence of a variable influences an
observed outcome. As measured by quantities such as the "average treatment
effect," this paradigm is employed across numerous biological fields, from
vaccine and drug development to policy interventions. Unfortunately, the
majority of these methods are often limited to univariate outcomes. Our work
generalizes causal estimands to outcomes with any number of dimensions or any
measurable space, and formulates traditional causal estimands for nominal
variables as causal discrepancy tests. We propose a simple technique for
adjusting universally consistent conditional independence tests and prove that
these tests are universally consistent causal discrepancy tests. Numerical
experiments illustrate that our method, Causal CDcorr, leads to improvements in
both finite sample validity and power when compared to existing strategies. Our
methods are all open source and available at github.com/ebridge2/cdcorr
Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics.
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Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome.
Peer reviewed: TrueAcknowledgements: BDP was supported by the NSF Graduate Research Fellowship (Grant no. DGE1746891). JTV was supported by the NSF CAREER Award (Grant no. 1942963). JTV was supported by the NSF NeuroNex Award (Grant no. 2014862). JTV and CEP were supported by the NIH BRAIN Initiative (Grant no. 1RF1MH123233-01). The authors thank members of the NeuroData lab for helpful feedback.Comparing connectomes can help explain how neural connectivity is related to genetics, disease, development, learning, and behavior. However, making statistical inferences about the significance and nature of differences between two networks is an open problem, and such analysis has not been extensively applied to nanoscale connectomes. Here, we investigate this problem via a case study on the bilateral symmetry of a larval Drosophila brain connectome. We translate notions of 'bilateral symmetry' to generative models of the network structure of the left and right hemispheres, allowing us to test and refine our understanding of symmetry. We find significant differences in connection probabilities both across the entire left and right networks and between specific cell types. By rescaling connection probabilities or removing certain edges based on weight, we also present adjusted definitions of bilateral symmetry exhibited by this connectome. This work shows how statistical inferences from networks can inform the study of connectomes, facilitating future comparisons of neural structures
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Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics.
Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to replicability is that multiple measurements of the same item (e.g., experimental sample or clinical participant) under fixed experimental constraints are relatively similar to one another. Thus, statistics that quantify the relative contributions of accidental deviations-such as measurement error-as compared to systematic deviations-such as individual differences-are critical. We demonstrate that existing replicability statistics, such as intra-class correlation coefficient and fingerprinting, fail to adequately differentiate between accidental and systematic deviations in very simple settings. We therefore propose a novel statistic, discriminability, which quantifies the degree to which an individual's samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean. Using this statistic, we introduce the possibility of optimizing experimental design via increasing discriminability and prove that optimizing discriminability improves performance bounds in subsequent inference tasks. In extensive simulated and real datasets (focusing on brain imaging and demonstrating on genomics), only optimizing data discriminability improves performance on all subsequent inference tasks for each dataset. We therefore suggest that designing experiments and analyses to optimize discriminability may be a crucial step in solving the replicability crisis, and more generally, mitigating accidental measurement error