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Filtering by: Committee Member Edwards, J. A. Remove constraint Committee Member: Edwards, J. A. Creator Sinha, Meghamala Remove constraint Creator: Sinha, Meghamala

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  • Analytical Methods for Improving Causal Inference

    Creator:
    Sinha, Meghamala
    Abstract:
    Causal inference is an important analytical tool to bridge the gap between prediction and decision-making. However, learning a causal network solely from data is a challenging task. In this work, various techniques have been explored for a better and improved causal network learning from data. Firstly, the problem of learning...
    Resource Type:
    Dissertation
    Full Text:
    : Analytical Methods for Improving Causal Inference Abstract approved: Stephen A. Ramsey Causal inference is

  • Causal Structure Learning from Experiments and Observations

    Creator:
    Sinha, Meghamala
    Abstract:
    In this research, we address the problem of learning a single causal network structure from multiple dataset generated from different experiments. The experiments can be observational or interventional. We assume that each dataset is generated by an unknown causal network altered under different experimental conditions (interventions, manipulation or perturbation). As...
    Resource Type:
    Masters Thesis
    Full Text:
    can be three possible explanations: Vi caused V j, V j caused Vi or both Vi and V j have a common