To perform causal inference in our case, we want to create a hypothetical scenario where the number of breaks is increased and all other factors that may influence productivity remain fixed. Then we can determine the causal effect of the additional break. When it is possible, randomized ...
It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.\nThe second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the ...
This approach may lead researchers to condition on variables that ultimately increase bias in the effect estimates. The use of directed acyclic graphs (DAGs) aids researchers in constructing thoughtful models based on hypothesised biologic mechanisms to produce the least biased effect estimates possible....