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  • Averaging causal estimators in high dimensions
    J. Causal Inference (IF 1.72) Pub Date : 2020-09-08
    Joseph Antonelli; Matthew Cefalu

    There has been increasing interest in recent years in the development of approaches to estimate causal effects when the number of potential confounders is prohibitively large. This growth in interest has led to a number of potential estimators one could use in this setting. Each of these estimators has different operating characteristics, and it is unlikely that one estimator will outperform all others

    更新日期:2020-09-08
  • The Inflation Technique Completely Solves the Causal Compatibility Problem
    J. Causal Inference (IF 1.72) Pub Date : 2020-09-03
    Miguel Navascués; Elie Wolfe

    The causal compatibility question asks whether a given causal structure graph — possibly involving latent variables — constitutes a genuinely plausible causal explanation for a given probability distribution over the graph’s observed categorical variables. Algorithms predicated on merely necessary constraints for causal compatibility typically suffer from false negatives, i.e. they admit incompatible

    更新日期:2020-09-08
  • Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial
    J. Causal Inference (IF 1.72) Pub Date : 2020-07-25
    Peter B. Gilbert; Bryan S. Blette; Bryan E. Shepherd; Michael G. Hudgens

    While the HVTN 505 trial showed no overall efficacy of the tested vaccine to prevent HIV infection over placebo, markers measuring immune response to vaccination were strongly correlated with infection. This finding generated the hypothesis that some marker-defined vaccinated subgroups were partially protected whereas others had their risk increased. This hypothesis can be assessed using the principal

    更新日期:2020-07-28
  • A Combinatorial Solution to Causal Compatibility
    J. Causal Inference (IF 1.72) Pub Date : 2020-07-25
    Thomas C. Fraser

    Within the field of causal inference, it is desirable to learn the structure of causal relationships holding between a system of variables from the correlations that these variables exhibit; a sub-problem of which is to certify whether or not a given causal hypothesis is compatible with the observed correlations. A particularly challenging setting for assessing causal compatibility is in the presence

    更新日期:2020-07-28
  • Unifying Gaussian LWF and AMP Chain Graphs to Model Interference
    J. Causal Inference (IF 1.72) Pub Date : 2019-11-05
    Jose M. Peña

    An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can represent both interference and non-interference relationships for Gaussian distributions

    更新日期:2019-11-05
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