With the development of biomedical techniques in the past decades, causal gene identification has become one of the most promising applications in human genome-based business, which can help doctors ...

The authors applied advanced causal inference methods and adjusted for confounding factors needed to ensure comparability between the groups defined by the intervention strategies. By using the Target ...

In this work, we present an effective approach for identifying causal genes from gene expression data by using a new search strategy based on non-linear regression-based independence tests, which is ...

Prediction and causal inference can appear very similar – not least because we can use linear regression in both cases. The distinction is not in the tools we use to answer the question, but the ...

CEM is a data preprocessing algorithm in causal inference which can construct your observational data into 'quasi' experimental data easily, mitigating the model dependency, bias, and inefficiency of ...

Our foray into causal analysis is not yet complete. Until we define the methods of causal inference, we can't get to the deeper insights that causal analysis can provide.