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Scientific challenge

A hypothesis is broadly described as a proposition expressing a belief about the world. This proposition defines certain expectations regarding observations, associations and causal relationships, based on previous empirical results, interventions or conclusions. 

Formulating a hypothesis is one of the most relevant steps in the scientific endeavor, and as such it is not an easy task.

Hypothesis formulation requires thorough knowledge of the subject matter and its theoretical framework, as well as expertise in research methods and techniques.

e[hypothesis] is designed to facilitate the process of hypothesis generation and formulation across a wide spectrum of scientific domains, utilizing a unique application of causal analysis.

Key features

  • Generate and formulate novel hypotheses to test previously unexplored associations in the data that could revolutionize research and innovation
  • Assign a level of confidence and significance to all hypotheses, supporting both Bayesian and Frequentist approaches
  • Provide causal analysis given specific hypotheses and the chosen datasets
  • Generate causal diagrams to illustrate the current knowledge and to represent formulated hypotheses
  • Use the multi-layer hypergraph to explore and exploit hypothesis-driven data interactions (synergistic or anti-synergistic) and generate multi-causal explanations
  • Generate hypotheses based on a fully systematized and automated process, enabling meaningful conversations with the data
  • Surface relevant connections and associations between disparate entities in a knowledge base
  • Perform effective and accurate hypothesis tests, enabling relevant analysis to demonstrate the potential of game-changing ideas
  • Measure the pertinence, uniqueness and novelty of any formulated hypothesis
  • Establish predictions of future knowledge and the foundation to support rigorous claims based on prior analyses

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